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1. The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores

Author

Weiner, MW, Aisen, P, Petersen, R, Jack, CR, Jagust, W, Trojanowki, JQ, Toga, AW, Beckett, L, Green, RC, Saykin, AJ, Morris, JC, Perrin, RJ, Shaw, LM, Khachaturian, Z, Carrillo, M, Potter, W, Barnes, L, Bernard, M, González, H, Ho, C, Hsiao, JK, Jackson, J, Masliah, E, Masterman, D, Okonkwo, O, Perrin, R, Ryan, L, Silverberg, N, Fleisher, A, Sacrey, DT, Fockler, J, Conti, C, Veitch, D, Neuhaus, J, Jin, C, Nosheny, R, Ashford, M, Flenniken, D, Kormos, A, Monine, T, Rafii, M, Raman, R, Jimenez, G, Donohue, M, Gessert, D, Salazar, J, Zimmerman, C, Cabrera, Y, Walter, S, Miller, G, Coker, G, Clanton, T, Hergesheimer, L, Smith, S, Adegoke, O, Mahboubi, P, Moore, S, Pizzola, J, Shaffer, E, Sloan, B, Harvey, D, Forghanian-Arani, A, Borowski, B, Ward, C, Schwarz, C, Jones, D, Gunter, J, Kantarci, K, Senjem, M, Vemuri, P, Reid, R, Fox, NC, Malone, I, Thompson, P, Thomopoulos, SI, Nir, TM, Jahanshad, N, DeCarli, C, Knaack, A, Fletcher, E, Tosun-Turgut, D, Chen, SR, Choe, M, Crawford, K, Yushkevich, PA, Das, S, Koeppe, RA, Reiman, EM, Chen, K, Mathis, C, Landau, S, Cairns, NJ, Householder, E, Franklin, E, Bernhardt, H, Taylor-Reinwald, L, Tojanowki, JQ, Korecka, M, and Figurski, M

Subjects
Biological Sciences, Genetics, Prevention, Human Genome, Biotechnology, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Knowledge Bases, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Risk Factors, Alzheimer’s Disease Neuroimaging Initiative, Biological sciences, Biomedical and clinical sciences
Abstract

The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu ) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.

Published
2022

2. Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders

Author

Hettwer, MD, Larivière, S, Park, BY, van den Heuvel, OA, Schmaal, L, Andreassen, OA, Ching, CRK, Hoogman, M, Buitelaar, J, van Rooij, D, Veltman, DJ, Stein, DJ, Franke, B, van Erp, TGM, Jahanshad, N, Thompson, PM, Thomopoulos, SI, Bethlehem, RAI, Bernhardt, BC, Eickhoff, SB, and Valk, SL

Subjects
Mental Health, Brain Disorders, Neurosciences, Behavioral and Social Science, Mental health, Humans, Cerebral Cortex, Connectome, Mental Disorders, Cerebellum, Attention, Magnetic Resonance Imaging, ENIGMA ADHD Working Group, ENIGMA Autism Working Group, ENIGMA Bipolar Disorder Working Group, ENIGMA Major Depression Working Group, ENIGMA OCD Working Group, ENIGMA Schizophrenia Working Group
Abstract

Neuropsychiatric disorders are increasingly conceptualized as overlapping spectra sharing multi-level neurobiological alterations. However, whether transdiagnostic cortical alterations covary in a biologically meaningful way is currently unknown. Here, we studied co-alteration networks across six neurodevelopmental and psychiatric disorders, reflecting pathological structural covariance. In 12,024 patients and 18,969 controls from the ENIGMA consortium, we observed that co-alteration patterns followed normative connectome organization and were anchored to prefrontal and temporal disease epicenters. Manifold learning revealed frontal-to-temporal and sensory/limbic-to-occipitoparietal transdiagnostic gradients, differentiating shared illness effects on cortical thickness along these axes. The principal gradient aligned with a normative cortical thickness covariance gradient and established a transcriptomic link to cortico-cerebello-thalamic circuits. Moreover, transdiagnostic gradients segregated functional networks involved in basic sensory, attentional/perceptual, and domain-general cognitive processes, and distinguished between regional cytoarchitectonic profiles. Together, our findings indicate that shared illness effects occur in a synchronized fashion and along multiple levels of hierarchical cortical organization.

Published
2022

3. Exploration of Shared Genetic Architecture Between Subcortical Brain Volumes and Anorexia Nervosa.

Author

Walton, E, Hibar, D, Yilmaz, Z, Jahanshad, N, Cheung, J, Batury, V-L, Seitz, J, Bulik, C, Thompson, P, and Ehrlich, Stefan

Subjects
Anorexia nervosa, Brain structure, Genetic correlation, Anorexia Nervosa, Brain, Case-Control Studies, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Magnetic Resonance Imaging, Mendelian Randomization Analysis, Organ Size, Polymorphism, Single Nucleotide
Abstract

In MRI scans of patients with anorexia nervosa (AN), reductions in brain volume are often apparent. However, it is unknown whether such brain abnormalities are influenced by genetic determinants that partially overlap with those underlying AN. Here, we used a battery of methods (LD score regression, genetic risk scores, sign test, SNP effect concordance analysis, and Mendelian randomization) to investigate the genetic covariation between subcortical brain volumes and risk for AN based on summary measures retrieved from genome-wide association studies of regional brain volumes (ENIGMA consortium, n = 13,170) and genetic risk for AN (PGC-ED consortium, n = 14,477). Genetic correlations ranged from - 0.10 to 0.23 (all p > 0.05). There were some signs of an inverse concordance between greater thalamus volume and risk for AN (permuted p = 0.009, 95% CI: [0.005, 0.017]). A genetic variant in the vicinity of ZW10, a gene involved in cell division, and neurotransmitter and immune system relevant genes, in particular DRD2, was significantly associated with AN only after conditioning on its association with caudate volume (pFDR = 0.025). Another genetic variant linked to LRRC4C, important in axonal and synaptic development, reached significance after conditioning on hippocampal volume (pFDR = 0.021). In this comprehensive set of analyses and based on the largest available sample sizes to date, there was weak evidence for associations between risk for AN and risk for abnormal subcortical brain volumes at a global level (that is, common variant genetic architecture), but suggestive evidence for effects of single genetic markers. Highly powered multimodal brain- and disorder-related genome-wide studies are needed to further dissect the shared genetic influences on brain structure and risk for AN.

Published
2019

4. Widespread white matter microstructural differences in schizophrenia across 4322 individuals: results from the ENIGMA Schizophrenia DTI Working Group

Author

Kelly, S, Jahanshad, N, Zalesky, A, Kochunov, P, Agartz, I, Alloza, C, Andreassen, OA, Arango, C, Banaj, N, Bouix, S, Bousman, CA, Brouwer, RM, Bruggemann, J, Bustillo, J, Cahn, W, Calhoun, V, Cannon, D, Carr, V, Catts, S, Chen, J, Chen, J-X, Chen, X, Chiapponi, C, Cho, Kl K, Ciullo, V, Corvin, AS, Crespo-Facorro, B, Cropley, V, De Rossi, P, Diaz-Caneja, CM, Dickie, EW, Ehrlich, S, Fan, F-M, Faskowitz, J, Fatouros-Bergman, H, Flyckt, L, Ford, JM, Fouche, J-P, Fukunaga, M, Gill, M, Glahn, DC, Gollub, R, Goudzwaard, ED, Guo, H, Gur, RE, Gur, RC, Gurholt, TP, Hashimoto, R, Hatton, SN, Henskens, FA, Hibar, DP, Hickie, IB, Hong, LE, Horacek, J, Howells, FM, Hulshoff Pol, HE, Hyde, CL, Isaev, D, Jablensky, A, Jansen, PR, Janssen, J, Jönsson, EG, Jung, LA, Kahn, RS, Kikinis, Z, Liu, K, Klauser, P, Knöchel, C, Kubicki, M, Lagopoulos, J, Langen, C, Lawrie, S, Lenroot, RK, Lim, KO, Lopez-Jaramillo, C, Lyall, A, Magnotta, V, Mandl, RCW, Mathalon, DH, McCarley, RW, McCarthy-Jones, S, McDonald, C, McEwen, S, McIntosh, A, Melicher, T, Mesholam-Gately, RI, Michie, PT, Mowry, B, Mueller, BA, Newell, DT, O'Donnell, P, Oertel-Knöchel, V, Oestreich, L, Paciga, SA, Pantelis, C, Pasternak, O, Pearlson, G, Pellicano, GR, Pereira, A, and Pineda Zapata, J

Subjects
Neurosciences, Brain Disorders, Mental Health, Serious Mental Illness, Biomedical Imaging, Schizophrenia, Clinical Research, Mental health, Adult, Aged, Aged, 80 and over, Brain, Cohort Studies, Corpus Callosum, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, White Matter, Young Adult, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

The regional distribution of white matter (WM) abnormalities in schizophrenia remains poorly understood, and reported disease effects on the brain vary widely between studies. In an effort to identify commonalities across studies, we perform what we believe is the first ever large-scale coordinated study of WM microstructural differences in schizophrenia. Our analysis consisted of 2359 healthy controls and 1963 schizophrenia patients from 29 independent international studies; we harmonized the processing and statistical analyses of diffusion tensor imaging (DTI) data across sites and meta-analyzed effects across studies. Significant reductions in fractional anisotropy (FA) in schizophrenia patients were widespread, and detected in 20 of 25 regions of interest within a WM skeleton representing all major WM fasciculi. Effect sizes varied by region, peaking at (d=0.42) for the entire WM skeleton, driven more by peripheral areas as opposed to the core WM where regions of interest were defined. The anterior corona radiata (d=0.40) and corpus callosum (d=0.39), specifically its body (d=0.39) and genu (d=0.37), showed greatest effects. Significant decreases, to lesser degrees, were observed in almost all regions analyzed. Larger effect sizes were observed for FA than diffusivity measures; significantly higher mean and radial diffusivity was observed for schizophrenia patients compared with controls. No significant effects of age at onset of schizophrenia or medication dosage were detected. As the largest coordinated analysis of WM differences in a psychiatric disorder to date, the present study provides a robust profile of widespread WM abnormalities in schizophrenia patients worldwide. Interactive three-dimensional visualization of the results is available at www.enigma-viewer.org.

Published
2018

5. Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group.

Author

Hibar, DP, Westlye, LT, Doan, NT, Jahanshad, N, Cheung, JW, Ching, CRK, Versace, A, Bilderbeck, AC, Uhlmann, A, Mwangi, B, Krämer, B, Overs, B, Hartberg, CB, Abé, C, Dima, D, Grotegerd, D, Sprooten, E, Bøen, E, Jimenez, E, Howells, FM, Delvecchio, G, Temmingh, H, Starke, J, Almeida, JRC, Goikolea, JM, Houenou, J, Beard, LM, Rauer, L, Abramovic, L, Bonnin, M, Ponteduro, MF, Keil, M, Rive, MM, Yao, N, Yalin, N, Najt, P, Rosa, PG, Redlich, R, Trost, S, Hagenaars, S, Fears, SC, Alonso-Lana, S, van Erp, TGM, Nickson, T, Chaim-Avancini, TM, Meier, TB, Elvsåshagen, T, Haukvik, UK, Lee, WH, Schene, AH, Lloyd, AJ, Young, AH, Nugent, A, Dale, AM, Pfennig, A, McIntosh, AM, Lafer, B, Baune, BT, Ekman, CJ, Zarate, CA, Bearden, CE, Henry, C, Simhandl, C, McDonald, C, Bourne, C, Stein, DJ, Wolf, DH, Cannon, DM, Glahn, DC, Veltman, DJ, Pomarol-Clotet, E, Vieta, E, Canales-Rodriguez, EJ, Nery, FG, Duran, FLS, Busatto, GF, Roberts, G, Pearlson, GD, Goodwin, GM, Kugel, H, Whalley, HC, Ruhe, HG, Soares, JC, Fullerton, JM, Rybakowski, JK, Savitz, J, Chaim, KT, Fatjó-Vilas, M, Soeiro-de-Souza, MG, Boks, MP, Zanetti, MV, Otaduy, MCG, Schaufelberger, MS, Alda, M, Ingvar, M, Phillips, ML, Kempton, MJ, Bauer, M, Landén, M, and Lawrence, NS

Subjects
Brain, Cerebral Cortex, Frontal Lobe, Prefrontal Cortex, Temporal Lobe, Humans, Magnetic Resonance Imaging, Case-Control Studies, Bipolar Disorder, Psychotic Disorders, Age Factors, Sex Factors, Adolescent, Adult, Middle Aged, Female, Male, Young Adult, Neuroimaging, Gray Matter, Clinical Research, Serious Mental Illness, Biomedical Imaging, Brain Disorders, Mental Health, Neurosciences, 2.1 Biological and endogenous factors, Aetiology, Mental health, Neurological, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Despite decades of research, the pathophysiology of bipolar disorder (BD) is still not well understood. Structural brain differences have been associated with BD, but results from neuroimaging studies have been inconsistent. To address this, we performed the largest study to date of cortical gray matter thickness and surface area measures from brain magnetic resonance imaging scans of 6503 individuals including 1837 unrelated adults with BD and 2582 unrelated healthy controls for group differences while also examining the effects of commonly prescribed medications, age of illness onset, history of psychosis, mood state, age and sex differences on cortical regions. In BD, cortical gray matter was thinner in frontal, temporal and parietal regions of both brain hemispheres. BD had the strongest effects on left pars opercularis (Cohen's d=-0.293; P=1.71 × 10-21), left fusiform gyrus (d=-0.288; P=8.25 × 10-21) and left rostral middle frontal cortex (d=-0.276; P=2.99 × 10-19). Longer duration of illness (after accounting for age at the time of scanning) was associated with reduced cortical thickness in frontal, medial parietal and occipital regions. We found that several commonly prescribed medications, including lithium, antiepileptic and antipsychotic treatment showed significant associations with cortical thickness and surface area, even after accounting for patients who received multiple medications. We found evidence of reduced cortical surface area associated with a history of psychosis but no associations with mood state at the time of scanning. Our analysis revealed previously undetected associations and provides an extensive analysis of potential confounding variables in neuroimaging studies of BD.

Published
2018

6. Prefrontal cortical thinning links to negative symptoms in schizophrenia via the ENIGMA consortium.

Author

Walton, E, Hibar, DP, van Erp, TGM, Potkin, SG, Roiz-Santiañez, R, Crespo-Facorro, B, Suarez-Pinilla, P, van Haren, NEM, de Zwarte, SMC, Kahn, RS, Cahn, W, Doan, NT, Jørgensen, KN, Gurholt, TP, Agartz, I, Andreassen, OA, Westlye, LT, Melle, I, Berg, AO, Morch-Johnsen, L, Færden, A, Flyckt, L, Fatouros-Bergman, H, Karolinska Schizophrenia Project Consortium (KaSP), Jönsson, EG, Hashimoto, R, Yamamori, H, Fukunaga, M, Jahanshad, N, De Rossi, P, Piras, F, Banaj, N, Spalletta, G, Gur, RE, Gur, RC, Wolf, DH, Satterthwaite, TD, Beard, LM, Sommer, IE, Koops, S, Gruber, O, Richter, A, Krämer, B, Kelly, S, Donohoe, G, McDonald, C, Cannon, DM, Corvin, A, Gill, M, Di Giorgio, A, Bertolino, A, Lawrie, S, Nickson, T, Whalley, HC, Neilson, E, Calhoun, VD, Thompson, PM, Turner, JA, and Ehrlich, S

Subjects
Karolinska Schizophrenia Project Consortium, Prefrontal Cortex, Humans, Magnetic Resonance Imaging, Linear Models, Schizophrenia, Psychiatric Status Rating Scales, Schizophrenic Psychology, Internationality, Image Processing, Computer-Assisted, Adult, Female, Male, Functional Laterality, Cortical thickness, ENIGMA, FreeSurfer, MRI, PANSS, SANS, medial orbitofrontal cortex, negative symptoms, schizophrenia, Image Processing, Computer-Assisted, Psychiatry, Psychology, Public Health and Health Services, Neurosciences
Abstract

BackgroundOur understanding of the complex relationship between schizophrenia symptomatology and etiological factors can be improved by studying brain-based correlates of schizophrenia. Research showed that impairments in value processing and executive functioning, which have been associated with prefrontal brain areas [particularly the medial orbitofrontal cortex (MOFC)], are linked to negative symptoms. Here we tested the hypothesis that MOFC thickness is associated with negative symptom severity.MethodsThis study included 1985 individuals with schizophrenia from 17 research groups around the world contributing to the ENIGMA Schizophrenia Working Group. Cortical thickness values were obtained from T1-weighted structural brain scans using FreeSurfer. A meta-analysis across sites was conducted over effect sizes from a model predicting cortical thickness by negative symptom score (harmonized Scale for the Assessment of Negative Symptoms or Positive and Negative Syndrome Scale scores).ResultsMeta-analytical results showed that left, but not right, MOFC thickness was significantly associated with negative symptom severity (β std = -0.075; p = 0.019) after accounting for age, gender, and site. This effect remained significant (p = 0.036) in a model including overall illness severity. Covarying for duration of illness, age of onset, antipsychotic medication or handedness weakened the association of negative symptoms with left MOFC thickness. As part of a secondary analysis including 10 other prefrontal regions further associations in the left lateral orbitofrontal gyrus and pars opercularis emerged.ConclusionsUsing an unusually large cohort and a meta-analytical approach, our findings point towards a link between prefrontal thinning and negative symptom severity in schizophrenia. This finding provides further insight into the relationship between structural brain abnormalities and negative symptoms in schizophrenia.

Published
2018

7. Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group

Author

Schmaal, L, Hibar, DP, Sämann, PG, Hall, GB, Baune, BT, Jahanshad, N, Cheung, JW, van Erp, TGM, Bos, D, Ikram, MA, Vernooij, MW, Niessen, WJ, Tiemeier, H, Hofman, A, Wittfeld, K, Grabe, HJ, Janowitz, D, Bülow, R, Selonke, M, Völzke, H, Grotegerd, D, Dannlowski, U, Arolt, V, Opel, N, Heindel, W, Kugel, H, Hoehn, D, Czisch, M, Couvy-Duchesne, B, Rentería, ME, Strike, LT, Wright, MJ, Mills, NT, de Zubicaray, GI, McMahon, KL, Medland, SE, Martin, NG, Gillespie, NA, Goya-Maldonado, R, Gruber, O, Krämer, B, Hatton, SN, Lagopoulos, J, Hickie, IB, Frodl, T, Carballedo, A, Frey, EM, van Velzen, LS, Penninx, BWJH, van Tol, M-J, van der Wee, NJ, Davey, CG, Harrison, BJ, Mwangi, B, Cao, B, Soares, JC, Veer, IM, Walter, H, Schoepf, D, Zurowski, B, Konrad, C, Schramm, E, Normann, C, Schnell, K, Sacchet, MD, Gotlib, IH, MacQueen, GM, Godlewska, BR, Nickson, T, McIntosh, AM, Papmeyer, M, Whalley, HC, Hall, J, Sussmann, JE, Li, M, Walter, M, Aftanas, L, Brack, I, Bokhan, NA, Thompson, PM, and Veltman, DJ

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Major Depressive Disorder, Serious Mental Illness, Neurosciences, Biomedical Imaging, Depression, Basic Behavioral and Social Science, Mental Health, Brain Disorders, Clinical Research, Pediatric, Behavioral and Social Science, 2.3 Psychological, social and economic factors, Aetiology, 2.1 Biological and endogenous factors, Neurological, Mental health, Adolescent, Adult, Brain, Cerebral Cortex, Depressive Disorder, Major, Female, Frontal Lobe, Gray Matter, Gyrus Cinguli, Humans, Magnetic Resonance Imaging, Male, Neuroimaging, Prefrontal Cortex, Temporal Lobe, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

The neuro-anatomical substrates of major depressive disorder (MDD) are still not well understood, despite many neuroimaging studies over the past few decades. Here we present the largest ever worldwide study by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Major Depressive Disorder Working Group on cortical structural alterations in MDD. Structural T1-weighted brain magnetic resonance imaging (MRI) scans from 2148 MDD patients and 7957 healthy controls were analysed with harmonized protocols at 20 sites around the world. To detect consistent effects of MDD and its modulators on cortical thickness and surface area estimates derived from MRI, statistical effects from sites were meta-analysed separately for adults and adolescents. Adults with MDD had thinner cortical gray matter than controls in the orbitofrontal cortex (OFC), anterior and posterior cingulate, insula and temporal lobes (Cohen's d effect sizes: -0.10 to -0.14). These effects were most pronounced in first episode and adult-onset patients (>21 years). Compared to matched controls, adolescents with MDD had lower total surface area (but no differences in cortical thickness) and regional reductions in frontal regions (medial OFC and superior frontal gyrus) and primary and higher-order visual, somatosensory and motor areas (d: -0.26 to -0.57). The strongest effects were found in recurrent adolescent patients. This highly powered global effort to identify consistent brain abnormalities showed widespread cortical alterations in MDD patients as compared to controls and suggests that MDD may impact brain structure in a highly dynamic way, with different patterns of alterations at different stages of life.

Published
2017

8. Subcortical brain structure and suicidal behaviour in major depressive disorder: a meta-analysis from the ENIGMA-MDD working group.

Author

Rentería, ME, Schmaal, L, Hibar, DP, Couvy-Duchesne, B, Strike, LT, Mills, NT, de Zubicaray, GI, McMahon, KL, Medland, SE, Gillespie, NA, Hatton, SN, Lagopoulos, J, Veltman, DJ, van der Wee, N, van Erp, TGM, Wittfeld, K, Grabe, HJ, Block, A, Hegenscheid, K, Völzke, H, Veer, IM, Walter, H, Schnell, K, Schramm, E, Normann, C, Schoepf, D, Konrad, C, Zurowski, B, Godlewska, BR, Cowen, PJ, Penninx, BWJH, Jahanshad, N, Thompson, PM, Wright, MJ, Martin, NG, Christensen, H, and Hickie, IB

Subjects
Brain, Humans, Magnetic Resonance Imaging, Prevalence, Risk Factors, Suicide, Depressive Disorder, Major, Adult, Aged, Middle Aged, Female, Male, Young Adult, Suicidal Ideation, Neuroimaging, Depressive Disorder, Major, Psychology, Clinical Sciences, Public Health and Health Services
Abstract

The aetiology of suicidal behaviour is complex, and knowledge about its neurobiological mechanisms is limited. Neuroimaging methods provide a noninvasive approach to explore the neural correlates of suicide vulnerability in vivo. The ENIGMA-MDD Working Group is an international collaboration evaluating neuroimaging and clinical data from thousands of individuals collected by research groups from around the world. Here we present analyses in a subset sample (n=3097) for whom suicidality data were available. Prevalence of suicidal symptoms among major depressive disorder (MDD) cases ranged between 29 and 69% across cohorts. We compared mean subcortical grey matter volumes, lateral ventricle volumes and total intracranial volume (ICV) in MDD patients with suicidal symptoms (N=451) vs healthy controls (N=1996) or MDD patients with no suicidal symptoms (N=650). MDD patients reporting suicidal plans or attempts showed a smaller ICV (P=4.12 × 10-3) or a 2.87% smaller volume compared with controls (Cohen's d=-0.284). In addition, we observed a nonsignificant trend in which MDD cases with suicidal symptoms had smaller subcortical volumes and larger ventricular volumes compared with controls. Finally, no significant differences (P=0.28-0.97) were found between MDD patients with and those without suicidal symptoms for any of the brain volume measures. This is by far the largest neuroimaging meta-analysis of suicidal behaviour in MDD to date. Our results did not replicate previous reports of association between subcortical brain structure and suicidality and highlight the need for collecting better-powered imaging samples and using improved suicidality assessment instruments.

Published
2017

9. Normative Modeling of Brain Morphometry in Clinical High Risk for Psychosis

Author

Haas, SS, Ge, R, Agartz, I, Amminger, GP, Andreassen, OA, Bachman, P, Baeza, I, Choi, S, Colibazzi, T, Cropley, VL, de la Fuente-Sandoval, C, Ebdrup, BH, Fortea, A, Fusar-Poli, P, Glenthoj, BY, Glenthoj, LB, Haut, KM, Hayes, RA, Heekeren, K, Hooker, CI, Hwang, WJ, Jahanshad, N, Kaess, M, Kasai, K, Katagiri, N, Kim, M, Kindler, J, Koike, S, Kristensen, TD, Kwon, JS, Lawrie, SM, Lebedeva, I, Lee, J, Lemmers-Jansen, ILJ, Lin, A, Ma, X, Mathalon, DH, McGuire, P, Michel, C, Mizrahi, R, Mizuno, M, Moller, P, Mora-Duran, R, Nelson, B, Nemoto, T, Nordentoft, M, Nordholm, D, Omelchenko, MA, Pantelis, C, Pariente, JC, Raghava, J, Reyes-Madrigal, F, Rossberg, JI, Rossler, W, Salisbury, DF, Sasabayashi, D, Schall, U, Smigielski, L, Sugranyes, G, Suzuki, M, Takahashi, T, Tamnes, CK, Theodoridou, A, Thomopoulos, SI, Thompson, PM, Tomyshev, AS, Uhlhaas, PJ, Vaernes, TG, van Amelsvoort, TAMJ, van Erp, TGM, Waltz, JA, Wenneberg, C, Westlye, LT, Wood, SJ, Zhou, JH, Hernaus, D, Jalbrzikowski, M, Kahn, RS, Corcoran, CM, Frangou, S, Haas, SS, Ge, R, Agartz, I, Amminger, GP, Andreassen, OA, Bachman, P, Baeza, I, Choi, S, Colibazzi, T, Cropley, VL, de la Fuente-Sandoval, C, Ebdrup, BH, Fortea, A, Fusar-Poli, P, Glenthoj, BY, Glenthoj, LB, Haut, KM, Hayes, RA, Heekeren, K, Hooker, CI, Hwang, WJ, Jahanshad, N, Kaess, M, Kasai, K, Katagiri, N, Kim, M, Kindler, J, Koike, S, Kristensen, TD, Kwon, JS, Lawrie, SM, Lebedeva, I, Lee, J, Lemmers-Jansen, ILJ, Lin, A, Ma, X, Mathalon, DH, McGuire, P, Michel, C, Mizrahi, R, Mizuno, M, Moller, P, Mora-Duran, R, Nelson, B, Nemoto, T, Nordentoft, M, Nordholm, D, Omelchenko, MA, Pantelis, C, Pariente, JC, Raghava, J, Reyes-Madrigal, F, Rossberg, JI, Rossler, W, Salisbury, DF, Sasabayashi, D, Schall, U, Smigielski, L, Sugranyes, G, Suzuki, M, Takahashi, T, Tamnes, CK, Theodoridou, A, Thomopoulos, SI, Thompson, PM, Tomyshev, AS, Uhlhaas, PJ, Vaernes, TG, van Amelsvoort, TAMJ, van Erp, TGM, Waltz, JA, Wenneberg, C, Westlye, LT, Wood, SJ, Zhou, JH, Hernaus, D, Jalbrzikowski, M, Kahn, RS, Corcoran, CM, and Frangou, S

Abstract

IMPORTANCE: The lack of robust neuroanatomical markers of psychosis risk has been traditionally attributed to heterogeneity. A complementary hypothesis is that variation in neuroanatomical measures in individuals at psychosis risk may be nested within the range observed in healthy individuals. OBJECTIVE: To quantify deviations from the normative range of neuroanatomical variation in individuals at clinical high risk for psychosis (CHR-P) and evaluate their overlap with healthy variation and their association with positive symptoms, cognition, and conversion to a psychotic disorder. DESIGN, SETTING, AND PARTICIPANTS: This case-control study used clinical-, IQ-, and neuroimaging software (FreeSurfer)-derived regional measures of cortical thickness (CT), cortical surface area (SA), and subcortical volume (SV) from 1340 individuals with CHR-P and 1237 healthy individuals pooled from 29 international sites participating in the Enhancing Neuroimaging Genetics Through Meta-analysis (ENIGMA) Clinical High Risk for Psychosis Working Group. Healthy individuals and individuals with CHR-P were matched on age and sex within each recruitment site. Data were analyzed between September 1, 2021, and November 30, 2022. MAIN OUTCOMES AND MEASURES: For each regional morphometric measure, deviation scores were computed as z scores indexing the degree of deviation from their normative means from a healthy reference population. Average deviation scores (ADS) were also calculated for regional CT, SA, and SV measures and globally across all measures. Regression analyses quantified the association of deviation scores with clinical severity and cognition, and 2-proportion z tests identified case-control differences in the proportion of individuals with infranormal (z < -1.96) or supranormal (z > 1.96) scores. RESULTS: Among 1340 individuals with CHR-P, 709 (52.91%) were male, and the mean (SD) age was 20.75 (4.74) years. Among 1237 healthy individuals, 684 (55.30%) were male, and the mean (SD

Published
2024

10. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures.

Author

Belov, V, Erwin-Grabner, T, Aghajani, M, Aleman, A, Amod, AR, Basgoze, Z, Benedetti, F, Besteher, B, Bülow, R, Ching, CRK, Connolly, CG, Cullen, K, Davey, CG, Dima, D, Dols, A, Evans, JW, Fu, CHY, Gonul, AS, Gotlib, IH, Grabe, HJ, Groenewold, N, Hamilton, JP, Harrison, BJ, Ho, TC, Mwangi, B, Jaworska, N, Jahanshad, N, Klimes-Dougan, B, Koopowitz, S-M, Lancaster, T, Li, M, Linden, DEJ, MacMaster, FP, Mehler, DMA, Melloni, E, Mueller, BA, Ojha, A, Oudega, ML, Penninx, BWJH, Poletti, S, Pomarol-Clotet, E, Portella, MJ, Pozzi, E, Reneman, L, Sacchet, MD, Sämann, PG, Schrantee, A, Sim, K, Soares, JC, Stein, DJ, Thomopoulos, SI, Uyar-Demir, A, van der Wee, NJA, van der Werff, SJA, Völzke, H, Whittle, S, Wittfeld, K, Wright, MJ, Wu, M-J, Yang, TT, Zarate, C, Veltman, DJ, Schmaal, L, Thompson, PM, Goya-Maldonado, R, ENIGMA Major Depressive Disorder working group, Belov, V, Erwin-Grabner, T, Aghajani, M, Aleman, A, Amod, AR, Basgoze, Z, Benedetti, F, Besteher, B, Bülow, R, Ching, CRK, Connolly, CG, Cullen, K, Davey, CG, Dima, D, Dols, A, Evans, JW, Fu, CHY, Gonul, AS, Gotlib, IH, Grabe, HJ, Groenewold, N, Hamilton, JP, Harrison, BJ, Ho, TC, Mwangi, B, Jaworska, N, Jahanshad, N, Klimes-Dougan, B, Koopowitz, S-M, Lancaster, T, Li, M, Linden, DEJ, MacMaster, FP, Mehler, DMA, Melloni, E, Mueller, BA, Ojha, A, Oudega, ML, Penninx, BWJH, Poletti, S, Pomarol-Clotet, E, Portella, MJ, Pozzi, E, Reneman, L, Sacchet, MD, Sämann, PG, Schrantee, A, Sim, K, Soares, JC, Stein, DJ, Thomopoulos, SI, Uyar-Demir, A, van der Wee, NJA, van der Werff, SJA, Völzke, H, Whittle, S, Wittfeld, K, Wright, MJ, Wu, M-J, Yang, TT, Zarate, C, Veltman, DJ, Schmaal, L, Thompson, PM, Goya-Maldonado, R, and ENIGMA Major Depressive Disorder working group

Abstract

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.

Published
2024

11. Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm.

Author

Jiang, Y, Luo, C, Wang, J, Palaniyappan, L, Chang, X, Xiang, S, Zhang, J, Duan, M, Huang, H, Gaser, C, Nemoto, K, Miura, K, Hashimoto, R, Westlye, LT, Richard, G, Fernandez-Cabello, S, Parker, N, Andreassen, OA, Kircher, T, Nenadić, I, Stein, F, Thomas-Odenthal, F, Teutenberg, L, Usemann, P, Dannlowski, U, Hahn, T, Grotegerd, D, Meinert, S, Lencer, R, Tang, Y, Zhang, T, Li, C, Yue, W, Zhang, Y, Yu, X, Zhou, E, Lin, C-P, Tsai, S-J, Rodrigue, AL, Glahn, D, Pearlson, G, Blangero, J, Karuk, A, Pomarol-Clotet, E, Salvador, R, Fuentes-Claramonte, P, Garcia-León, MÁ, Spalletta, G, Piras, F, Vecchio, D, Banaj, N, Cheng, J, Liu, Z, Yang, J, Gonul, AS, Uslu, O, Burhanoglu, BB, Uyar Demir, A, Rootes-Murdy, K, Calhoun, VD, Sim, K, Green, M, Quidé, Y, Chung, YC, Kim, W-S, Sponheim, SR, Demro, C, Ramsay, IS, Iasevoli, F, de Bartolomeis, A, Barone, A, Ciccarelli, M, Brunetti, A, Cocozza, S, Pontillo, G, Tranfa, M, Park, MTM, Kirschner, M, Georgiadis, F, Kaiser, S, Van Rheenen, TE, Rossell, SL, Hughes, M, Woods, W, Carruthers, SP, Sumner, P, Ringin, E, Spaniel, F, Skoch, A, Tomecek, D, Homan, P, Homan, S, Omlor, W, Cecere, G, Nguyen, DD, Preda, A, Thomopoulos, SI, Jahanshad, N, Cui, L-B, Yao, D, Thompson, PM, Turner, JA, van Erp, TGM, Cheng, W, ENIGMA Schizophrenia Consortium, Feng, J, ZIB Consortium, Jiang, Y, Luo, C, Wang, J, Palaniyappan, L, Chang, X, Xiang, S, Zhang, J, Duan, M, Huang, H, Gaser, C, Nemoto, K, Miura, K, Hashimoto, R, Westlye, LT, Richard, G, Fernandez-Cabello, S, Parker, N, Andreassen, OA, Kircher, T, Nenadić, I, Stein, F, Thomas-Odenthal, F, Teutenberg, L, Usemann, P, Dannlowski, U, Hahn, T, Grotegerd, D, Meinert, S, Lencer, R, Tang, Y, Zhang, T, Li, C, Yue, W, Zhang, Y, Yu, X, Zhou, E, Lin, C-P, Tsai, S-J, Rodrigue, AL, Glahn, D, Pearlson, G, Blangero, J, Karuk, A, Pomarol-Clotet, E, Salvador, R, Fuentes-Claramonte, P, Garcia-León, MÁ, Spalletta, G, Piras, F, Vecchio, D, Banaj, N, Cheng, J, Liu, Z, Yang, J, Gonul, AS, Uslu, O, Burhanoglu, BB, Uyar Demir, A, Rootes-Murdy, K, Calhoun, VD, Sim, K, Green, M, Quidé, Y, Chung, YC, Kim, W-S, Sponheim, SR, Demro, C, Ramsay, IS, Iasevoli, F, de Bartolomeis, A, Barone, A, Ciccarelli, M, Brunetti, A, Cocozza, S, Pontillo, G, Tranfa, M, Park, MTM, Kirschner, M, Georgiadis, F, Kaiser, S, Van Rheenen, TE, Rossell, SL, Hughes, M, Woods, W, Carruthers, SP, Sumner, P, Ringin, E, Spaniel, F, Skoch, A, Tomecek, D, Homan, P, Homan, S, Omlor, W, Cecere, G, Nguyen, DD, Preda, A, Thomopoulos, SI, Jahanshad, N, Cui, L-B, Yao, D, Thompson, PM, Turner, JA, van Erp, TGM, Cheng, W, ENIGMA Schizophrenia Consortium, Feng, J, and ZIB Consortium

Abstract

Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal 'trajectory' of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Published
2024

12. Large-scale analysis of structural brain asymmetries during neurodevelopment : Associations with age and sex in 4265 children and adolescents.

Author

Kurth, F, Schijven, D, van den Heuvel, O A, Hoogman, M, van Rooij, D, Stein, D J, Buitelaar, J K, Bölte, S, Auzias, G, Kushki, A, Venkatasubramanian, G, Rubia, K, Bollmann, S, Isaksson, J, Jaspers-Fayer, F, Marsh, R, Batistuzzo, M C, Arnold, P D, Bressan, R A, Stewart, S E, Gruner, P, Sorensen, L, Pan, P M, Silk, T J, Gur, R C, Cubillo, A I, Haavik, J, O'Gorman Tuura, R L, Hartman, C A, Calvo, R, McGrath, J, Calderoni, S, Jackowski, A, Chantiluke, K C, Satterthwaite, T D, Busatto, G F, Nigg, J T, Gur, R E, Retico, A, Tosetti, M, Gallagher, L, Szeszko, P R, Neufeld, J, Ortiz, A E, Ghisleni, C, Lazaro, L, Hoekstra, P J, Anagnostou, E, Hoekstra, L, Simpson, B, Plessen, J K, Deruelle, C, Soreni, N, James, A, Narayanaswamy, J, Reddy, J Y, Fitzgerald, J, Bellgrove, M A, Salum, G A, Janssen, J, Muratori, F, Vila, M, Giral, M Garcia, Ameis, S H, Bosco, P, Remnélius, K Lundin, Huyser, C, Pariente, J C, Jalbrzikowski, M, Rosa, P G, O'Hearn, K M, Ehrlich, S, Mollon, J, Zugman, A, Christakou, A, Arango, C, Fisher, S E, Kong, X, Franke, B, Medland, S E, Thomopoulos, S I, Jahanshad, N, Glahn, D C, Thompson, P M, Francks, C, Luders, E, Kurth, F, Schijven, D, van den Heuvel, O A, Hoogman, M, van Rooij, D, Stein, D J, Buitelaar, J K, Bölte, S, Auzias, G, Kushki, A, Venkatasubramanian, G, Rubia, K, Bollmann, S, Isaksson, J, Jaspers-Fayer, F, Marsh, R, Batistuzzo, M C, Arnold, P D, Bressan, R A, Stewart, S E, Gruner, P, Sorensen, L, Pan, P M, Silk, T J, Gur, R C, Cubillo, A I, Haavik, J, O'Gorman Tuura, R L, Hartman, C A, Calvo, R, McGrath, J, Calderoni, S, Jackowski, A, Chantiluke, K C, Satterthwaite, T D, Busatto, G F, Nigg, J T, Gur, R E, Retico, A, Tosetti, M, Gallagher, L, Szeszko, P R, Neufeld, J, Ortiz, A E, Ghisleni, C, Lazaro, L, Hoekstra, P J, Anagnostou, E, Hoekstra, L, Simpson, B, Plessen, J K, Deruelle, C, Soreni, N, James, A, Narayanaswamy, J, Reddy, J Y, Fitzgerald, J, Bellgrove, M A, Salum, G A, Janssen, J, Muratori, F, Vila, M, Giral, M Garcia, Ameis, S H, Bosco, P, Remnélius, K Lundin, Huyser, C, Pariente, J C, Jalbrzikowski, M, Rosa, P G, O'Hearn, K M, Ehrlich, S, Mollon, J, Zugman, A, Christakou, A, Arango, C, Fisher, S E, Kong, X, Franke, B, Medland, S E, Thomopoulos, S I, Jahanshad, N, Glahn, D C, Thompson, P M, Francks, C, and Luders, E

Abstract

Only a small number of studies have assessed structural differences between the two hemispheres during childhood and adolescence. However, the existing findings lack consistency or are restricted to a particular brain region, a specific brain feature, or a relatively narrow age range. Here, we investigated associations between brain asymmetry and age as well as sex in one of the largest pediatric samples to date (n = 4265), aged 1-18 years, scanned at 69 sites participating in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our study revealed that significant brain asymmetries already exist in childhood, but their magnitude and direction depend on the brain region examined and the morphometric measurement used (cortical volume or thickness, regional surface area, or subcortical volume). With respect to effects of age, some asymmetries became weaker over time while others became stronger; sometimes they even reversed direction. With respect to sex differences, the total number of regions exhibiting significant asymmetries was larger in females than in males, while the total number of measurements indicating significant asymmetries was larger in males (as we obtained more than one measurement per cortical region). The magnitude of the significant asymmetries was also greater in males. However, effect sizes for both age effects and sex differences were small. Taken together, these findings suggest that cerebral asymmetries are an inherent organizational pattern of the brain that manifests early in life. Overall, brain asymmetry appears to be relatively stable throughout childhood and adolescence, with some differential effects in males and females.

Published
2024
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13. Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation

Author

Ge, R., Yu, Y, Qi, Y.X., Fan, Y.N., Chen, S., Gao, Chuntong, Haas, S.S., New, F., Boomsma, D.I., Brodaty, H., Brouwer, R.M., Buckner, R., Caseras, X., Crivello, F., Crone, E.A.M., Erk, S., Fisher, S.E., Franke, B., Glahn, D.C., Dannlowski, U., Grotegerd, D., Gruber, O., Pol, H.E. Hulshoff, Schumann, G., Tamnes, C.K., Walter, H., Wierenga, L.M., Jahanshad, N., Thompson, P.M., Frangou, S., Ge, R., Yu, Y, Qi, Y.X., Fan, Y.N., Chen, S., Gao, Chuntong, Haas, S.S., New, F., Boomsma, D.I., Brodaty, H., Brouwer, R.M., Buckner, R., Caseras, X., Crivello, F., Crone, E.A.M., Erk, S., Fisher, S.E., Franke, B., Glahn, D.C., Dannlowski, U., Grotegerd, D., Gruber, O., Pol, H.E. Hulshoff, Schumann, G., Tamnes, C.K., Walter, H., Wierenga, L.M., Jahanshad, N., Thompson, P.M., and Frangou, S.

Abstract

Contains fulltext : 305173.pdf (Publisher’s version ) (Open Access), The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3-90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.

Published
2024

14. Response to Dr Fried & Dr Kievit, and Dr Malhi et al.

Author

Schmaal, L, Veltman, DJ, van Erp, TGM, Sämann, PG, Frodl, T, Jahanshad, N, Loehrer, E, Vernooij, MW, Niessen, WJ, Ikram, MA, Wittfeld, K, Grabe, HJ, Block, A, Hegenscheid, K, Hoehn, D, Czisch, M, Lagopoulos, J, Hatton, SN, Hickie, IB, Goya-Maldonado, R, Krämer, B, Gruber, O, Couvy-Duchesne, B, Rentería, ME, Strike, LT, Wright, MJ, de Zubicaray, GI, McMahon, KL, Medland, SE, Gillespie, NA, Hall, GB, van Velzen, LS, van Tol, M-J, van der Wee, NJ, Veer, IM, Walter, H, Schramm, E, Normann, C, Schoepf, D, Konrad, C, Zurowski, B, McIntosh, AM, Whalley, HC, Sussmann, JE, Godlewska, BR, Fischer, FH, Penninx, BWJH, Thompson, PM, and Hibar, DP

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical and Health Psychology, Clinical Sciences, Psychology, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Published
2016

15. Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group.

Author

Schmaal, L, Veltman, DJ, van Erp, TGM, Sämann, PG, Frodl, T, Jahanshad, N, Loehrer, E, Tiemeier, H, Hofman, A, Niessen, WJ, Vernooij, MW, Ikram, MA, Wittfeld, K, Grabe, HJ, Block, A, Hegenscheid, K, Völzke, H, Hoehn, D, Czisch, M, Lagopoulos, J, Hatton, SN, Hickie, IB, Goya-Maldonado, R, Krämer, B, Gruber, O, Couvy-Duchesne, B, Rentería, ME, Strike, LT, Mills, NT, de Zubicaray, GI, McMahon, KL, Medland, SE, Martin, NG, Gillespie, NA, Wright, MJ, Hall, GB, MacQueen, GM, Frey, EM, Carballedo, A, van Velzen, LS, van Tol, MJ, van der Wee, NJ, Veer, IM, Walter, H, Schnell, K, Schramm, E, Normann, C, Schoepf, D, Konrad, C, Zurowski, B, Nickson, T, McIntosh, AM, Papmeyer, M, Whalley, HC, Sussmann, JE, Godlewska, BR, Cowen, PJ, Fischer, FH, Rose, M, Penninx, BWJH, Thompson, PM, and Hibar, DP

Subjects
Brain, Hippocampus, Humans, Magnetic Resonance Imaging, Case-Control Studies, Depressive Disorder, Major, Adult, Middle Aged, Female, Male, Neuroimaging, major depressive disorder, structural volumes, ENIGMA consortium, meta-analysis, hippocampus, Depressive Disorder, Major, Psychiatry, Medical and Health Sciences, Biological Sciences, Psychology and Cognitive Sciences
Abstract

The pattern of structural brain alterations associated with major depressive disorder (MDD) remains unresolved. This is in part due to small sample sizes of neuroimaging studies resulting in limited statistical power, disease heterogeneity and the complex interactions between clinical characteristics and brain morphology. To address this, we meta-analyzed three-dimensional brain magnetic resonance imaging data from 1728 MDD patients and 7199 controls from 15 research samples worldwide, to identify subcortical brain volumes that robustly discriminate MDD patients from healthy controls. Relative to controls, patients had significantly lower hippocampal volumes (Cohen's d=-0.14, % difference=-1.24). This effect was driven by patients with recurrent MDD (Cohen's d=-0.17, % difference=-1.44), and we detected no differences between first episode patients and controls. Age of onset ⩽21 was associated with a smaller hippocampus (Cohen's d=-0.20, % difference=-1.85) and a trend toward smaller amygdala (Cohen's d=-0.11, % difference=-1.23) and larger lateral ventricles (Cohen's d=0.12, % difference=5.11). Symptom severity at study inclusion was not associated with any regional brain volumes. Sample characteristics such as mean age, proportion of antidepressant users and proportion of remitted patients, and methodological characteristics did not significantly moderate alterations in brain volumes in MDD. Samples with a higher proportion of antipsychotic medication users showed larger caudate volumes in MDD patients compared with controls. This currently largest worldwide effort to identify subcortical brain alterations showed robust smaller hippocampal volumes in MDD patients, moderated by age of onset and first episode versus recurrent episode status.

Published
2016

16. Commonfolate gene variant, MTHFR C677T, is associated with brain structure in two independent cohorts of people with mild cognitive impairment

Author

Weiner, Michael, Rajagopalan, P, Jahanshad, N, Stein, JL, Hua, X, Madsen, SK, Kohannim, O, Hibar, DP, Toga, AW, Jack, CR, and Saykin, AJ

Abstract

A commonly carried C677T polymorphism in a folate-related gene, MTHFR, is associated with higher plasma homocysteine, a well-known mediator of neuronal damage and brain atrophy. As homocysteine promotes brain atrophy, we set out to discover whether people

Published
2012

17. Disrupted brain networks in the aging HIV+ population.

Author

Jahanshad, N, Valcour, VG, Nir, TM, Kohannim, O, Busovaca, E, Nicolas, K, and Thompson, P

Abstract

Anti-retroviral therapies have become widely available, and as a result, individuals infected with the human immunodeficiency virus (HIV) are living longer, and becoming integrated into the geriatric population. Around half of the HIV+ population show some degree of cognitive impairment, but it is unknown how their neural networks and brain connectivity compare to those of non-infected people. Here we combined magnetic resonance imaging based cortical parcellations with high angular resolution diffusion tensor imaging (HARDI) tractography in 55 HIV-seropositive patients and 30 age-matched controls, to map white matter connections between cortical regions. We set out to determine selective virus-associated disruptions in the brain's structural network. All individuals in this study were aged 60-80, with full access to antiretroviral therapy. Frontal and motor connections were compromised in HIV+ individuals. HIV+ people who carried the ApoE4 genotype - which puts them at even greater risk for neurodegeneration - showed additional network structure deficits in temporal and parietal connections. The ApoE4 genotype interacted with duration of illness. Carriers showed greater brain network inefficiencies, the longer they were infected. Neural network deficiencies in HIV+ populations exceed those typical of normal aging, and are worse in those genetically predisposed to brain degeneration. This work isolates neuropathological alterations in HIV+ elders, even when treated with anti-retroviral therapy. Network impairments may contribute to the neuropsychological abnormalities in elderly HIV patients, who will soon account for around half of all HIV+ adults.

Published
2012

18. Reliability of Structural Connectivity Examined with Four Different Diffusion Reconstruction Methods at Two Different Spatial and Angular Resolutions

Author

Villalon-Reina, J. E., Nir, T. M., Zhan, L., McMahon, K. L., de Zubicaray, G. I., Wright, M. J., Jahanshad, N., Thompson, P. M., Hege, Hans-Christian, Series editor, Hoffman, David, Series editor, Johnson, Christopher R., Series editor, Polthier, Konrad, Series editor, Rumpf, Martin, Series editor, Fuster, Andrea, editor, Ghosh, Aurobrata, editor, Kaden, Enrico, editor, Rathi, Yogesh, editor, and Reisert, Marco, editor

Published
2016
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19. Volume of subcortical brain regions in social anxiety disorder: mega-analytic results from 37 samples in the ENIGMA-Anxiety Working Group.

Author

Groenewold, NA, Bas-Hoogendam, JM, Amod, AR, Laansma, MA, Van Velzen, LS, Aghajani, M, Hilbert, K, Oh, H, Salas, R, Jackowski, AP, Pan, PM, Salum, GA, Blair, JR, Blair, KS, Hirsch, J, Pantazatos, SP, Schneier, FR, Talati, A, Roelofs, K, Volman, I, Blanco-Hinojo, L, Cardoner, N, Pujol, J, Beesdo-Baum, K, Ching, CRK, Thomopoulos, SI, Jansen, A, Kircher, T, Krug, A, Nenadić, I, Stein, F, Dannlowski, U, Grotegerd, D, Lemke, H, Meinert, S, Winter, A, Erb, M, Kreifelts, B, Gong, Q, Lui, S, Zhu, F, Mwangi, B, Soares, JC, Wu, M-J, Bayram, A, Canli, M, Tükel, R, Westenberg, PM, Heeren, A, Cremers, HR, Hofmann, D, Straube, T, Doruyter, AGG, Lochner, C, Peterburs, J, Van Tol, M-J, Gur, RE, Kaczkurkin, AN, Larsen, B, Satterthwaite, TD, Filippi, CA, Gold, AL, Harrewijn, A, Zugman, A, Bülow, R, Grabe, HJ, Völzke, H, Wittfeld, K, Böhnlein, J, Dohm, K, Kugel, H, Schrammen, E, Zwanzger, P, Leehr, EJ, Sindermann, L, Ball, TM, Fonzo, GA, Paulus, MP, Simmons, A, Stein, MB, Klumpp, H, Phan, KL, Furmark, T, Månsson, KNT, Manzouri, A, Avery, SN, Blackford, JU, Clauss, JA, Feola, B, Harper, JC, Sylvester, CM, Lueken, U, Veltman, DJ, Winkler, AM, Jahanshad, N, Pine, DS, Thompson, PM, Stein, DJ, Van der Wee, NJA, Groenewold, NA, Bas-Hoogendam, JM, Amod, AR, Laansma, MA, Van Velzen, LS, Aghajani, M, Hilbert, K, Oh, H, Salas, R, Jackowski, AP, Pan, PM, Salum, GA, Blair, JR, Blair, KS, Hirsch, J, Pantazatos, SP, Schneier, FR, Talati, A, Roelofs, K, Volman, I, Blanco-Hinojo, L, Cardoner, N, Pujol, J, Beesdo-Baum, K, Ching, CRK, Thomopoulos, SI, Jansen, A, Kircher, T, Krug, A, Nenadić, I, Stein, F, Dannlowski, U, Grotegerd, D, Lemke, H, Meinert, S, Winter, A, Erb, M, Kreifelts, B, Gong, Q, Lui, S, Zhu, F, Mwangi, B, Soares, JC, Wu, M-J, Bayram, A, Canli, M, Tükel, R, Westenberg, PM, Heeren, A, Cremers, HR, Hofmann, D, Straube, T, Doruyter, AGG, Lochner, C, Peterburs, J, Van Tol, M-J, Gur, RE, Kaczkurkin, AN, Larsen, B, Satterthwaite, TD, Filippi, CA, Gold, AL, Harrewijn, A, Zugman, A, Bülow, R, Grabe, HJ, Völzke, H, Wittfeld, K, Böhnlein, J, Dohm, K, Kugel, H, Schrammen, E, Zwanzger, P, Leehr, EJ, Sindermann, L, Ball, TM, Fonzo, GA, Paulus, MP, Simmons, A, Stein, MB, Klumpp, H, Phan, KL, Furmark, T, Månsson, KNT, Manzouri, A, Avery, SN, Blackford, JU, Clauss, JA, Feola, B, Harper, JC, Sylvester, CM, Lueken, U, Veltman, DJ, Winkler, AM, Jahanshad, N, Pine, DS, Thompson, PM, Stein, DJ, and Van der Wee, NJA

Abstract

There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample, SAD patients showed smaller bilateral putamen volume than controls (left: d = -0.077, pFWE = 0.037; right: d = -0.104, pFWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = -0.034, pFWE = 0.045). Smaller bilateral putamen volumes (left: d = -0.141, pFWE < 0.001; right: d = -0.158, pFWE < 0.001) and larger bilateral pallidum volumes (left: d = 0.129, pFWE = 0.006; right: d = 0.099, pFWE = 0.046) were detected in adult SAD patients relative to controls, but no volumetric differences were apparent in adolescent SAD patients relative to controls. Comorbid anxiety disorders and age of SAD onset were additional determinants of SAD-related volumetric differences in subcortical regions. To conclude, subtle volumetric alterations in subcortical regions in SAD were detected. Heterogeneity in age and clinical characteristics may partly explain inconsistencies in previous findings. The association between alterations in subcortical volumes and SAD illness progression deserves further investigation, especially from adolescence into adulthood.

Published
2023

20. Progressive Spinal Cord Degeneration in Friedreich's Ataxia: Results from ENIGMA-Ataxia.

Author

Rezende, TJR, Adanyeguh, IM, Arrigoni, F, Bender, B, Cendes, F, Corben, LA, Deistung, A, Delatycki, M, Dogan, I, Egan, GF, Göricke, SL, Georgiou-Karistianis, N, Henry, P-G, Hutter, D, Jahanshad, N, Joers, JM, Lenglet, C, Lindig, T, Martinez, ARM, Martinuzzi, A, Paparella, G, Peruzzo, D, Reetz, K, Romanzetti, S, Schöls, L, Schulz, JB, Synofzik, M, Thomopoulos, SI, Thompson, PM, Timmann, D, Harding, IH, França, MC, Rezende, TJR, Adanyeguh, IM, Arrigoni, F, Bender, B, Cendes, F, Corben, LA, Deistung, A, Delatycki, M, Dogan, I, Egan, GF, Göricke, SL, Georgiou-Karistianis, N, Henry, P-G, Hutter, D, Jahanshad, N, Joers, JM, Lenglet, C, Lindig, T, Martinez, ARM, Martinuzzi, A, Paparella, G, Peruzzo, D, Reetz, K, Romanzetti, S, Schöls, L, Schulz, JB, Synofzik, M, Thomopoulos, SI, Thompson, PM, Timmann, D, Harding, IH, and França, MC

Published
2023

21. Cortical profiles of numerous psychiatric disorders and normal development share a common pattern.

Author

Cao, Z, Cupertino, RB, Ottino-Gonzalez, J, Murphy, A, Pancholi, D, Juliano, A, Chaarani, B, Albaugh, M, Yuan, D, Schwab, N, Stafford, J, Goudriaan, AE, Hutchison, K, Li, C-SR, Luijten, M, Groefsema, M, Momenan, R, Schmaal, L, Sinha, R, van Holst, RJ, Veltman, DJ, Wiers, RW, Porjesz, B, Lett, T, Banaschewski, T, Bokde, ALW, Desrivières, S, Flor, H, Grigis, A, Gowland, P, Heinz, A, Brühl, R, Martinot, J-L, Martinot, M-LP, Artiges, E, Nees, F, Orfanos, DP, Paus, T, Poustka, L, Hohmann, S, Millenet, S, Fröhner, JH, Robinson, L, Smolka, MN, Walter, H, Winterer, J, Schumann, G, Whelan, R, Bhatt, RR, Zhu, A, Conrod, P, Jahanshad, N, Thompson, PM, Mackey, S, Garavan, H, IMAGEN Consortium, ENIGMA Addiction Working Group, Cao, Z, Cupertino, RB, Ottino-Gonzalez, J, Murphy, A, Pancholi, D, Juliano, A, Chaarani, B, Albaugh, M, Yuan, D, Schwab, N, Stafford, J, Goudriaan, AE, Hutchison, K, Li, C-SR, Luijten, M, Groefsema, M, Momenan, R, Schmaal, L, Sinha, R, van Holst, RJ, Veltman, DJ, Wiers, RW, Porjesz, B, Lett, T, Banaschewski, T, Bokde, ALW, Desrivières, S, Flor, H, Grigis, A, Gowland, P, Heinz, A, Brühl, R, Martinot, J-L, Martinot, M-LP, Artiges, E, Nees, F, Orfanos, DP, Paus, T, Poustka, L, Hohmann, S, Millenet, S, Fröhner, JH, Robinson, L, Smolka, MN, Walter, H, Winterer, J, Schumann, G, Whelan, R, Bhatt, RR, Zhu, A, Conrod, P, Jahanshad, N, Thompson, PM, Mackey, S, Garavan, H, IMAGEN Consortium, and ENIGMA Addiction Working Group

Abstract

The neurobiological bases of the association between development and psychopathology remain poorly understood. Here, we identify a shared spatial pattern of cortical thickness (CT) in normative development and several psychiatric and neurological disorders. Principal component analysis (PCA) was applied to CT of 68 regions in the Desikan-Killiany atlas derived from three large-scale datasets comprising a total of 41,075 neurotypical participants. PCA produced a spatially broad first principal component (PC1) that was reproducible across datasets. Then PC1 derived from healthy adult participants was compared to the pattern of CT differences associated with psychiatric and neurological disorders comprising a total of 14,886 cases and 20,962 controls from seven ENIGMA disease-related working groups, normative maturation and aging comprising a total of 17,697 scans from the ABCD Study® and the IMAGEN developmental study, and 17,075 participants from the ENIGMA Lifespan working group, as well as gene expression maps from the Allen Human Brain Atlas. Results revealed substantial spatial correspondences between PC1 and widespread lower CT observed in numerous psychiatric disorders. Moreover, the PC1 pattern was also correlated with the spatial pattern of normative maturation and aging. The transcriptional analysis identified a set of genes including KCNA2, KCNS1 and KCNS2 with expression patterns closely related to the spatial pattern of PC1. The gene category enrichment analysis indicated that the transcriptional correlations of PC1 were enriched to multiple gene ontology categories and were specifically over-represented starting at late childhood, coinciding with the onset of significant cortical maturation and emergence of psychopathology during the prepubertal-to-pubertal transition. Collectively, the present study reports a reproducible latent pattern of CT that captures interregional profiles of cortical changes in both normative brain maturation and a spectrum of ps

Published
2023

22. Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium.

Author

Schijven, Dick, Postema, M.C., Fukunaga, M., Matsumoto, J., Miura, K., Zwarte, S.M.C. de, Haren, N.E.M. van, Cahn, W., Hulshoff Pol, H.E., Kahn, R.S., Ayesa-Arriola, R., Ortoz-García de la Foz, V., Tordesillas-Gutierrez, D., Vázquez-Bourgon, J., Crespo-Facorro, B., Alnæs, D., Dahl, A., Westlye, L.T., Agartz, I., Andreassen, O.A., Jönsson, E.G., Kochunov, P., Bruggemann, J.M., Catts, S.V., Michie, P.T., Mowry, B.J., Quidé, Y., Rasser, P.E., Schall, U., Scott, R.J., Carr, V.J., Green, M.J., Henskens, F.A., Loughland, C.M., Pantelis, C., Weickert, C.S., Weickert, T.W., Haan, L. de, Brosch, K., Pfarr, J.K., Ringwald, K.G., Stein, F., Jansen, Andreas, Kircher, T.T.J., Nenadić, I., Krämer, Bernd, Gruber, O., Satterthwaite, T.D., Bustillo, J., Mathalon, D.H., Preda, A., Calhoun, V.D., Ford, J.M., Potkin, S.G., Chen, Jingxu, Tan, Yunlong, Wang, Zhiren, Xiang, Hong, Fan, Fengmei, Bernardoni, F., Ehrlich, S., Fuentes-Claramonte, P., Garcia-Leon, M.A., Guerrero-Pedraza, A., Salvador, R., Sarró, S., Pomarol-Clotet, E., Ciullo, V., Piras, F., Vecchio, D., Banaj, N., Spalletta, G., Michielse, S., Amelsvoort, T. van, Dickie, E.W., Voineskos, A.N., Sim, K., Ciufolini, S., Dazzan, P., Murray, R.M., Kim, W.S., Chung, Y.C., Andreou, C., Schmidt, A, Borgwardt, S., McIntosh, A.M., Whalley, H.C., Lawrie, S.M., Plessis, S. du, Luckhoff, H.K., Scheffler, F., Emsley, R., Grotegerd, D., Lencer, R., Dannlowski, U., Edmond, J.T., Rootes-Murdy, K., Stephen, J.M., Mayer, A.R., Antonucci, L.A., Fazio, L., Pergola, G., Bertolino, A., Díaz-Caneja, C.M., Janssen, J, Lois, N.G., Arango, C., Tomyshev, A.S., Lebedeva, I., Cervenka, S., Sellgren, C.M., Georgiadis, F., Kirschner, M., Kaiser, S., Hajek, T., Skoch, A., Spaniel, F., Kim, M., Kwak, Y.B., Oh, S., Kwon, J.S., James, A., Bakker, G., Knöchel, C., Stäblein, M., Oertel, V., Uhlmann, A., Howells, F.M., Stein, D.J., Temmingh, H.S., Diaz-Zuluaga, A.M., Pineda-Zapata, J.A., López-Jaramillo, C., Homan, S., Ji, E., Surbeck, W., Homan, P., Fisher, S.E., Franke, B., Glahn, D.C., Gur, R.C., Hashimoto, R., Jahanshad, N., Luders, E., Medland, S.E., Thompson, P.M., Turner, J.A., Erp, T.G. van, Francks, C., Schijven, Dick, Postema, M.C., Fukunaga, M., Matsumoto, J., Miura, K., Zwarte, S.M.C. de, Haren, N.E.M. van, Cahn, W., Hulshoff Pol, H.E., Kahn, R.S., Ayesa-Arriola, R., Ortoz-García de la Foz, V., Tordesillas-Gutierrez, D., Vázquez-Bourgon, J., Crespo-Facorro, B., Alnæs, D., Dahl, A., Westlye, L.T., Agartz, I., Andreassen, O.A., Jönsson, E.G., Kochunov, P., Bruggemann, J.M., Catts, S.V., Michie, P.T., Mowry, B.J., Quidé, Y., Rasser, P.E., Schall, U., Scott, R.J., Carr, V.J., Green, M.J., Henskens, F.A., Loughland, C.M., Pantelis, C., Weickert, C.S., Weickert, T.W., Haan, L. de, Brosch, K., Pfarr, J.K., Ringwald, K.G., Stein, F., Jansen, Andreas, Kircher, T.T.J., Nenadić, I., Krämer, Bernd, Gruber, O., Satterthwaite, T.D., Bustillo, J., Mathalon, D.H., Preda, A., Calhoun, V.D., Ford, J.M., Potkin, S.G., Chen, Jingxu, Tan, Yunlong, Wang, Zhiren, Xiang, Hong, Fan, Fengmei, Bernardoni, F., Ehrlich, S., Fuentes-Claramonte, P., Garcia-Leon, M.A., Guerrero-Pedraza, A., Salvador, R., Sarró, S., Pomarol-Clotet, E., Ciullo, V., Piras, F., Vecchio, D., Banaj, N., Spalletta, G., Michielse, S., Amelsvoort, T. van, Dickie, E.W., Voineskos, A.N., Sim, K., Ciufolini, S., Dazzan, P., Murray, R.M., Kim, W.S., Chung, Y.C., Andreou, C., Schmidt, A, Borgwardt, S., McIntosh, A.M., Whalley, H.C., Lawrie, S.M., Plessis, S. du, Luckhoff, H.K., Scheffler, F., Emsley, R., Grotegerd, D., Lencer, R., Dannlowski, U., Edmond, J.T., Rootes-Murdy, K., Stephen, J.M., Mayer, A.R., Antonucci, L.A., Fazio, L., Pergola, G., Bertolino, A., Díaz-Caneja, C.M., Janssen, J, Lois, N.G., Arango, C., Tomyshev, A.S., Lebedeva, I., Cervenka, S., Sellgren, C.M., Georgiadis, F., Kirschner, M., Kaiser, S., Hajek, T., Skoch, A., Spaniel, F., Kim, M., Kwak, Y.B., Oh, S., Kwon, J.S., James, A., Bakker, G., Knöchel, C., Stäblein, M., Oertel, V., Uhlmann, A., Howells, F.M., Stein, D.J., Temmingh, H.S., Diaz-Zuluaga, A.M., Pineda-Zapata, J.A., López-Jaramillo, C., Homan, S., Ji, E., Surbeck, W., Homan, P., Fisher, S.E., Franke, B., Glahn, D.C., Gur, R.C., Hashimoto, R., Jahanshad, N., Luders, E., Medland, S.E., Thompson, P.M., Turner, J.A., Erp, T.G. van, and Francks, C.

Abstract

Item does not contain fulltext, Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.

Published
2023

23. Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium

Author

Constantinides, C, Han, LKM, Alloza, C, Antonucci, LA, Arango, C, Ayesa-Arriola, R, Banaj, N, Bertolino, A, Borgwardt, S, Bruggemann, J, Bustillo, J, Bykhovski, O, Calhoun, V, Carr, V, Catts, S, Chung, Y-C, Crespo-Facorro, B, Diaz-Caneja, CM, Donohoe, G, Du Plessis, S, Edmond, J, Ehrlich, S, Emsley, R, Eyler, LT, Fuentes-Claramonte, P, Georgiadis, F, Green, M, Guerrero-Pedraza, A, Ha, M, Hahn, T, Henskens, FA, Holleran, L, Homan, S, Homan, P, Jahanshad, N, Janssen, J, Ji, E, Kaiser, S, Kaleda, V, Kim, M, Kim, W-S, Kirschner, M, Kochunov, P, Kwak, YB, Kwon, JS, Lebedeva, I, Liu, J, Mitchie, P, Michielse, S, Mothersill, D, Mowry, B, de la Foz, VO-G, Pantelis, C, Pergola, G, Piras, F, Pomarol-Clotet, E, Preda, A, Quide, Y, Rasser, PE, Rootes-Murdy, K, Salvador, R, Sangiuliano, M, Sarro, S, Schall, U, Schmidt, A, Scott, RJ, Selvaggi, P, Sim, K, Skoch, A, Spalletta, G, Spaniel, F, Thomopoulos, S, Tomecek, D, Tomyshev, AS, Tordesillas-Gutierrez, D, van Amelsvoort, T, Vazquez-Bourgon, J, Vecchio, D, Voineskos, A, Weickert, CS, Weickert, T, Thompson, PM, Schmaal, L, van Erp, TGM, Turner, J, Cole, JH, Dima, D, Walton, E, Constantinides, C, Han, LKM, Alloza, C, Antonucci, LA, Arango, C, Ayesa-Arriola, R, Banaj, N, Bertolino, A, Borgwardt, S, Bruggemann, J, Bustillo, J, Bykhovski, O, Calhoun, V, Carr, V, Catts, S, Chung, Y-C, Crespo-Facorro, B, Diaz-Caneja, CM, Donohoe, G, Du Plessis, S, Edmond, J, Ehrlich, S, Emsley, R, Eyler, LT, Fuentes-Claramonte, P, Georgiadis, F, Green, M, Guerrero-Pedraza, A, Ha, M, Hahn, T, Henskens, FA, Holleran, L, Homan, S, Homan, P, Jahanshad, N, Janssen, J, Ji, E, Kaiser, S, Kaleda, V, Kim, M, Kim, W-S, Kirschner, M, Kochunov, P, Kwak, YB, Kwon, JS, Lebedeva, I, Liu, J, Mitchie, P, Michielse, S, Mothersill, D, Mowry, B, de la Foz, VO-G, Pantelis, C, Pergola, G, Piras, F, Pomarol-Clotet, E, Preda, A, Quide, Y, Rasser, PE, Rootes-Murdy, K, Salvador, R, Sangiuliano, M, Sarro, S, Schall, U, Schmidt, A, Scott, RJ, Selvaggi, P, Sim, K, Skoch, A, Spalletta, G, Spaniel, F, Thomopoulos, S, Tomecek, D, Tomyshev, AS, Tordesillas-Gutierrez, D, van Amelsvoort, T, Vazquez-Bourgon, J, Vecchio, D, Voineskos, A, Weickert, CS, Weickert, T, Thompson, PM, Schmaal, L, van Erp, TGM, Turner, J, Cole, JH, Dima, D, and Walton, E

Abstract

Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18-72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18-73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.55 years (95% CI: 2.91, 4.19; I2 = 57.53%) compared to controls, after adjusting for age, sex and site (Cohen's d = 0.48). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions.

Published
2023

24. Large- scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium

Author

Schijven, D, Postema, MC, Fukunaga, M, Matsumoto, J, Miura, K, de Zwarte, SMC, van Haren, NEM, Cahn, W, Pol, HEH, Kahn, RS, Ayesa-Arriola, R, de la Foz, VO-G, Tordesillas-Gutierrez, D, Vazquez-Bourgon, J, Crespo-Facorro, B, Alnaes, D, Dahl, A, Westlye, LT, Agartz, I, Andreassen, OA, Jonsson, EG, Kochunov, P, Bruggemann, JM, Catts, SV, Michie, PT, Mowry, BJ, Quide, Y, Rasser, PE, Schall, U, Scott, RJ, Carr, VJ, Green, MJ, Henskens, FA, Loughland, CM, Pantelis, C, Weickert, CS, Weickert, TW, De Haan, L, Brosch, K, Pfarr, J-K, Ringwald, KG, Stein, F, Jansen, A, Kircher, TTJ, Nenadic, I, Kramer, B, Gruber, O, Satterthwaite, TD, Bustillo, J, Mathalon, DH, Preda, A, Calhoun, VD, Ford, JM, Potkin, SG, Chen, J, Tan, Y, Wang, Z, Xiang, H, Fan, F, Bernardoni, F, Ehrlich, S, Fuentes-Claramonte, P, Garcia-Leon, MA, Guerrero-Pedraza, A, Salvador, R, Sarro, S, Pomarol-Clotet, E, Ciullo, V, Piras, F, Vecchio, D, Banaj, N, Spalletta, G, Michielse, S, van Amelsvoort, T, Dickie, EW, Voineskos, AN, Sim, K, Ciufolini, S, Dazzan, P, Murray, RM, Kim, W-S, Chung, Y-C, Andreou, C, Schmidt, A, Borgwardt, S, McIntosh, AM, Whalley, HC, Lawrie, SM, Du Plessis, S, Luckhoff, HK, Scheffler, F, Emsley, R, Grotegerd, D, Lencer, R, Dannlowski, U, Edmond, JT, Rootes-Murdy, K, Stephen, JM, Mayer, AR, Antonucci, LA, Fazio, L, Pergola, G, Bertolino, A, Diaz-Caneja, CM, Janssen, J, Lois, NG, Arango, C, Tomyshev, AS, Lebedeva, I, Cervenkav, S, Sellgrenv, CM, Georgiadis, F, Kirschner, M, Kaiser, S, Hajek, T, Skoch, A, Spaniel, F, Kim, M, Bin Kwak, Y, Oh, S, Kwon, JS, James, A, Bakker, G, Knochel, C, Stablein, M, Oertel, V, Uhlmann, A, Howells, FM, Stein, DJ, Temmingh, HS, Diaz-Zuluaga, AM, Pineda-Zapata, JA, Lopez-Jaramillo, C, Homan, S, Ji, E, Surbeck, W, Homan, P, Fishera, SE, Franke, B, Glahn, DC, Gur, RC, Hashimoto, R, Jahanshad, N, Luders, E, Medland, SE, Thompson, PM, Turner, JA, van Erp, TGM, Francks, C, Schijven, D, Postema, MC, Fukunaga, M, Matsumoto, J, Miura, K, de Zwarte, SMC, van Haren, NEM, Cahn, W, Pol, HEH, Kahn, RS, Ayesa-Arriola, R, de la Foz, VO-G, Tordesillas-Gutierrez, D, Vazquez-Bourgon, J, Crespo-Facorro, B, Alnaes, D, Dahl, A, Westlye, LT, Agartz, I, Andreassen, OA, Jonsson, EG, Kochunov, P, Bruggemann, JM, Catts, SV, Michie, PT, Mowry, BJ, Quide, Y, Rasser, PE, Schall, U, Scott, RJ, Carr, VJ, Green, MJ, Henskens, FA, Loughland, CM, Pantelis, C, Weickert, CS, Weickert, TW, De Haan, L, Brosch, K, Pfarr, J-K, Ringwald, KG, Stein, F, Jansen, A, Kircher, TTJ, Nenadic, I, Kramer, B, Gruber, O, Satterthwaite, TD, Bustillo, J, Mathalon, DH, Preda, A, Calhoun, VD, Ford, JM, Potkin, SG, Chen, J, Tan, Y, Wang, Z, Xiang, H, Fan, F, Bernardoni, F, Ehrlich, S, Fuentes-Claramonte, P, Garcia-Leon, MA, Guerrero-Pedraza, A, Salvador, R, Sarro, S, Pomarol-Clotet, E, Ciullo, V, Piras, F, Vecchio, D, Banaj, N, Spalletta, G, Michielse, S, van Amelsvoort, T, Dickie, EW, Voineskos, AN, Sim, K, Ciufolini, S, Dazzan, P, Murray, RM, Kim, W-S, Chung, Y-C, Andreou, C, Schmidt, A, Borgwardt, S, McIntosh, AM, Whalley, HC, Lawrie, SM, Du Plessis, S, Luckhoff, HK, Scheffler, F, Emsley, R, Grotegerd, D, Lencer, R, Dannlowski, U, Edmond, JT, Rootes-Murdy, K, Stephen, JM, Mayer, AR, Antonucci, LA, Fazio, L, Pergola, G, Bertolino, A, Diaz-Caneja, CM, Janssen, J, Lois, NG, Arango, C, Tomyshev, AS, Lebedeva, I, Cervenkav, S, Sellgrenv, CM, Georgiadis, F, Kirschner, M, Kaiser, S, Hajek, T, Skoch, A, Spaniel, F, Kim, M, Bin Kwak, Y, Oh, S, Kwon, JS, James, A, Bakker, G, Knochel, C, Stablein, M, Oertel, V, Uhlmann, A, Howells, FM, Stein, DJ, Temmingh, HS, Diaz-Zuluaga, AM, Pineda-Zapata, JA, Lopez-Jaramillo, C, Homan, S, Ji, E, Surbeck, W, Homan, P, Fishera, SE, Franke, B, Glahn, DC, Gur, RC, Hashimoto, R, Jahanshad, N, Luders, E, Medland, SE, Thompson, PM, Turner, JA, van Erp, TGM, and Francks, C

Abstract

Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.

Published
2023

25. Brain deficit patterns of metabolic illnesses overlap with those for major depressive disorder: A new metric of brain metabolic disease.

Author

Hatch, KS, Gao, S, Ma, Y, Russo, A, Jahanshad, N, Thompson, PM, Adhikari, BM, Bruce, H, Van der Vaart, A, Sotiras, A, Kvarta, MD, Nichols, TE, Schmaal, L, Hong, LE, Kochunov, P, Hatch, KS, Gao, S, Ma, Y, Russo, A, Jahanshad, N, Thompson, PM, Adhikari, BM, Bruce, H, Van der Vaart, A, Sotiras, A, Kvarta, MD, Nichols, TE, Schmaal, L, Hong, LE, and Kochunov, P

Abstract

Metabolic illnesses (MET) are detrimental to brain integrity and are common comorbidities in patients with mental illnesses, including major depressive disorder (MDD). We quantified effects of MET on standard regional brain morphometric measures from 3D brain MRI as well as diffusion MRI in a large sample of UK BioBank participants. The pattern of regional effect sizes of MET in non-psychiatric UKBB subjects was significantly correlated with the spatial profile of regional effects reported by the largest meta-analyses in MDD but not in bipolar disorder, schizophrenia or Alzheimer's disease. We used a regional vulnerability index (RVI) for MET (RVI-MET) to measure individual's brain similarity to the expected patterns in MET in the UK Biobank sample. Subjects with MET showed a higher effect size for RVI-MET than for any of the individual brain measures. We replicated elevation of RVI-MET in a sample of MDD participants with MET versus non-MET. RVI-MET scores were significantly correlated with the volume of white matter hyperintensities, a neurological consequence of MET and age, in both groups. Higher RVI-MET in both samples was associated with obesity, tobacco smoking and frequent alcohol use but was unrelated to antidepressant use. In summary, MET effects on the brain were regionally specific and individual similarity to the pattern was more strongly associated with MET than any regional brain structural metric. Effects of MET overlapped with the reported brain differences in MDD, likely due to higher incidence of MET, smoking and alcohol use in subjects with MDD.

Published
2023

27. Lack of structural brain alteration associated with insomnia: Findings from the ENIGMA-Sleep working group

Author

Weihs, A., primary, Bi, H., additional, Bülow, R., additional, Eickhoff, S.B., additional, Ewert, R., additional, Frenzel, S., additional, Grabe, H.J., additional, Hoffstaedter, F., additional, Jahanshad, N., additional, Khazaie, H., additional, Patil, K., additional, Riemann, D., additional, Rostampour, M., additional, Schiel, J.E., additional, Spiegelhalder, K., additional, Stubbe, B., additional, Thomopoulos, S.I., additional, Thompson, P.M., additional, Valk, S.L., additional, Völzke, H., additional, Zarei, M., additional, and Tahmasian, M., additional

Published
2022
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29. Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders

Author

Hettwer, M. D., Larivière, S., Veltman, D. J., Stein, D. J., Franke, B., van Erp, T. G. M., van Rooij, D., van den Heuvel, O. A., Jahanshad, N., Thompson, P. M., Thomopoulos, S. I., Park, B. Y., Bethlehem, R. A. I., Bernhardt, B. C., Eickhoff, S. B., Valk, S. L., Schmaal, L., Andreassen, O. A., Ching, C. R. K., Hoogman, M., Buitelaar, J., ENIGMA ADHD Working Group, ENIGMA Autism Working Group, ENIGMA Bipolar Disorder Working Group, ENIGMA Major Depression Working Group, ENIGMA OCD Working Group, ENIGMA Schizophrenia Working Group, Anatomy and neurosciences, Psychiatry, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Amsterdam Neuroscience - Neurodegeneration, Amsterdam Neuroscience - Brain Imaging, and Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep

Subjects
Cerebral Cortex, Multidisciplinary, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], Mental Disorders, General Physics and Astronomy, General Chemistry, Magnetic Resonance Imaging, General Biochemistry, Genetics and Molecular Biology, All institutes and research themes of the Radboud University Medical Center, 130 000 Cognitive Neurology & Memory, Cerebellum, Connectome, Humans, Attention, ddc:500
Abstract

Neuropsychiatric disorders are increasingly conceptualized as overlapping spectra sharing multi-level neurobiological alterations. However, whether transdiagnostic cortical alterations covary in a biologically meaningful way is currently unknown. Here, we studied co-alteration networks across six neurodevelopmental and psychiatric disorders, reflecting pathological structural covariance. In 12,024 patients and 18,969 controls from the ENIGMA consortium, we observed that co-alteration patterns followed normative connectome organization and were anchored to prefrontal and temporal disease epicenters. Manifold learning revealed frontal-to-temporal and sensory/limbic-to-occipitoparietal transdiagnostic gradients, differentiating shared illness effects on cortical thickness along these axes. The principal gradient aligned with a normative cortical thickness covariance gradient and established a transcriptomic link to cortico-cerebello-thalamic circuits. Moreover, transdiagnostic gradients segregated functional networks involved in basic sensory, attentional/perceptual, and domain-general cognitive processes, and distinguished between regional cytoarchitectonic profiles. Together, our findings indicate that shared illness effects occur in a synchronized fashion and along multiple levels of hierarchical cortical organization.

Published
2022

30. Cross disorder comparisons of brain structure in schizophrenia, bipolar disorder, major depressive disorder, and 22q11.2 deletion syndrome: A review of ENIGMA findings

Author

Cheon, E-J, Bearden, CE, Sun, D, Ching, CRK, Andreassen, OA, Schmaal, L, Veltman, DJ, Thomopoulos, S, Kochunov, P, Jahanshad, N, Thompson, PM, Turner, JA, van Erp, TGM, Cheon, E-J, Bearden, CE, Sun, D, Ching, CRK, Andreassen, OA, Schmaal, L, Veltman, DJ, Thomopoulos, S, Kochunov, P, Jahanshad, N, Thompson, PM, Turner, JA, and van Erp, TGM

Abstract

This review compares the main brain abnormalities in schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and 22q11.2 Deletion Syndrome (22q11DS) determined by ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium investigations. We obtained ranked effect sizes for subcortical volumes, regional cortical thickness, cortical surface area, and diffusion tensor imaging abnormalities, comparing each of these disorders relative to healthy controls. In addition, the studies report on significant associations between brain imaging metrics and disorder-related factors such as symptom severity and treatments. Visual comparison of effect size profiles shows that effect sizes are generally in the same direction and scale in severity with the disorders (in the order SZ > BD > MDD). The effect sizes for 22q11DS, a rare genetic syndrome that increases the risk for psychiatric disorders, appear to be much larger than for either of the complex psychiatric disorders. This is consistent with the idea of generally larger effects on the brain of rare compared to common genetic variants. Cortical thickness and surface area effect sizes for 22q11DS with psychosis compared to 22q11DS without psychosis are more similar to those of SZ and BD than those of MDD; a pattern not observed for subcortical brain structures and fractional anisotropy effect sizes. The observed similarities in effect size profiles for cortical measures across the psychiatric disorders mimic those observed for shared genetic variance between these disorders reported based on family and genetic studies and are consistent with shared genetic risk for SZ and BD and structural brain phenotypes.

Published
2022

31. Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years

Author

Dima, D., Modabbernia, A., Papachristou, E., Doucet, G.E., Agartz, I., Aghajani, M., Akudjedu, T.N., Albajes-Eizagirre, A., Alnaes, D., Alpert, K.I., Andersson, M., Andreasen, N.C., Andreassen, O.A., Asherson, P., Banaschewski, T., Bargallo, N., Baumeister, S., Baur-Streubel, R., Bertolino, A., Bonvino, A., Boomsma, D.I., Borgwardt, S., Bourque, J., Brandeis, D., Breier, A., Brodaty, H., Brouwer, R.M., Buitelaar, J.K., Busatto, G.F., Buckner, R.L., Calhoun, V., Canales-Rodríguez, E.J., Cannon, D.M., Caseras, X., Castellanos, F.X., Cervenka, S., Chaim-Avancini, T.M., Ching, C.R., Chubar, V., Clark, V.P., Conrod, P., Conzelmann, A., Crespo-Facorro, B., Crivello, F., Crone, E.A.M., Dannlowski, U., Dale, A.M., Davey, C., Geus, E.J. de, Haan, L. de, Zubicaray, G.I. de, Braber, A., Dickie, E.W., Giorgio, A. Di, Doan, N.T., Dørum, E.S., Ehrlich, S., Erk, S., Espeseth, T., Fatouros-Bergman, H., Fisher, S.E., Fouche, J.P., Franke, B., Frodl, T., Fuentes-Claramonte, P., Glahn, D.C., Gotlib, I.H., Grabe, H.J., Grimm, O., Groenewold, N.A., Grotegerd, D., Gruber, O., Gruner, P., Gur, R.E., Gur, R.C., Hahn, T., Harrison, B.J., Hartman, Catharina A., Hatton, S.N., Heinz, A., Heslenfeld, D.J., Hibar, D.P., Hickie, I.B., Ho, B.C.H., Hoekstra, P.J., Hohmann, S., Holmes, A.J., Hoogman, M., Hosten, N., Howells, F.M., Pol, H.E.H., Huyser, C., Jahanshad, N., James, A., Jernigan, T.L., Jiang, J., Jönsson, E.G., Joska, J.A., Kahn, R., Kalnin, A., Naaijen, J., Klein, M., Thompson, P.M., Frangou, S., Dima, D., Modabbernia, A., Papachristou, E., Doucet, G.E., Agartz, I., Aghajani, M., Akudjedu, T.N., Albajes-Eizagirre, A., Alnaes, D., Alpert, K.I., Andersson, M., Andreasen, N.C., Andreassen, O.A., Asherson, P., Banaschewski, T., Bargallo, N., Baumeister, S., Baur-Streubel, R., Bertolino, A., Bonvino, A., Boomsma, D.I., Borgwardt, S., Bourque, J., Brandeis, D., Breier, A., Brodaty, H., Brouwer, R.M., Buitelaar, J.K., Busatto, G.F., Buckner, R.L., Calhoun, V., Canales-Rodríguez, E.J., Cannon, D.M., Caseras, X., Castellanos, F.X., Cervenka, S., Chaim-Avancini, T.M., Ching, C.R., Chubar, V., Clark, V.P., Conrod, P., Conzelmann, A., Crespo-Facorro, B., Crivello, F., Crone, E.A.M., Dannlowski, U., Dale, A.M., Davey, C., Geus, E.J. de, Haan, L. de, Zubicaray, G.I. de, Braber, A., Dickie, E.W., Giorgio, A. Di, Doan, N.T., Dørum, E.S., Ehrlich, S., Erk, S., Espeseth, T., Fatouros-Bergman, H., Fisher, S.E., Fouche, J.P., Franke, B., Frodl, T., Fuentes-Claramonte, P., Glahn, D.C., Gotlib, I.H., Grabe, H.J., Grimm, O., Groenewold, N.A., Grotegerd, D., Gruber, O., Gruner, P., Gur, R.E., Gur, R.C., Hahn, T., Harrison, B.J., Hartman, Catharina A., Hatton, S.N., Heinz, A., Heslenfeld, D.J., Hibar, D.P., Hickie, I.B., Ho, B.C.H., Hoekstra, P.J., Hohmann, S., Holmes, A.J., Hoogman, M., Hosten, N., Howells, F.M., Pol, H.E.H., Huyser, C., Jahanshad, N., James, A., Jernigan, T.L., Jiang, J., Jönsson, E.G., Joska, J.A., Kahn, R., Kalnin, A., Naaijen, J., Klein, M., Thompson, P.M., and Frangou, S.

Abstract

Contains fulltext : 245411.pdf (Publisher’s version ) (Open Access), Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.

Published
2022

32. Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years

Author

Frangou, S., Modabbernia, A., Williams, S.C.R., Papachristou, E., Doucet, G.E., Agartz, I., Aghajani, M., Akudjedu, T.N., Albajes-Eizagirre, A., Alnaes, D., Alpert, K.I., Andersson, M., Andreasen, N.C., Andreassen, O.A., Asherson, P., Banaschewski, T., Bargallo, N., Baumeister, S., Baur-Streubel, R., Bertolino, A., Bonvino, A., Boomsma, D.I., Borgwardt, S., Bourque, J., Brandeis, D., Breier, A., Brodaty, H., Brouwer, R.M., Buitelaar, J.K., Busatto, G.F., Buckner, R.L., Calhoun, V., Canales-Rodríguez, E.J., Cannon, D.M., Caseras, X., Castellanos, F.X., Cervenka, S., Chaim-Avancini, T.M., Ching, C.R., Chubar, V., Clark, V.P., Conrod, P., Conzelmann, A., Crespo-Facorro, B., Crivello, F., Crone, E.A.M., Dale, A.M., Dannlowski, U., Davey, C., Geus, E.J. de, Haan, L. de, Zubicaray, G.I. de, Braber, A., Dickie, E.W., Giorgio, A. Di, Doan, N.T., Dørum, E.S., Ehrlich, S., Erk, S., Espeseth, T., Fatouros-Bergman, H., Fisher, S.E., Fouche, J.P., Franke, B., Frodl, T., Fuentes-Claramonte, P., Glahn, D.C., Gotlib, I.H., Grabe, H.J., Grimm, O., Groenewold, N.A., Grotegerd, D., Gruber, O., Gruner, P., Gur, R.E., Gur, R.C., Hahn, T., Harrison, B.J., Hartman, Catharina A., Hatton, S.N., Heinz, A., Heslenfeld, D.J., Hibar, D.P., Hickie, I.B., Ho, B.C.H., Hoekstra, P.J., Hohmann, S., Holmes, A.J., Hoogman, M., Hosten, N., Howells, F.M., Pol, H.E.H., Huyser, C., Jahanshad, N., James, A., Jernigan, T.L., Jiang, J., Jönsson, E.G., Joska, J.A., Kahn, R., Klein, M., Frangou, S., Modabbernia, A., Williams, S.C.R., Papachristou, E., Doucet, G.E., Agartz, I., Aghajani, M., Akudjedu, T.N., Albajes-Eizagirre, A., Alnaes, D., Alpert, K.I., Andersson, M., Andreasen, N.C., Andreassen, O.A., Asherson, P., Banaschewski, T., Bargallo, N., Baumeister, S., Baur-Streubel, R., Bertolino, A., Bonvino, A., Boomsma, D.I., Borgwardt, S., Bourque, J., Brandeis, D., Breier, A., Brodaty, H., Brouwer, R.M., Buitelaar, J.K., Busatto, G.F., Buckner, R.L., Calhoun, V., Canales-Rodríguez, E.J., Cannon, D.M., Caseras, X., Castellanos, F.X., Cervenka, S., Chaim-Avancini, T.M., Ching, C.R., Chubar, V., Clark, V.P., Conrod, P., Conzelmann, A., Crespo-Facorro, B., Crivello, F., Crone, E.A.M., Dale, A.M., Dannlowski, U., Davey, C., Geus, E.J. de, Haan, L. de, Zubicaray, G.I. de, Braber, A., Dickie, E.W., Giorgio, A. Di, Doan, N.T., Dørum, E.S., Ehrlich, S., Erk, S., Espeseth, T., Fatouros-Bergman, H., Fisher, S.E., Fouche, J.P., Franke, B., Frodl, T., Fuentes-Claramonte, P., Glahn, D.C., Gotlib, I.H., Grabe, H.J., Grimm, O., Groenewold, N.A., Grotegerd, D., Gruber, O., Gruner, P., Gur, R.E., Gur, R.C., Hahn, T., Harrison, B.J., Hartman, Catharina A., Hatton, S.N., Heinz, A., Heslenfeld, D.J., Hibar, D.P., Hickie, I.B., Ho, B.C.H., Hoekstra, P.J., Hohmann, S., Holmes, A.J., Hoogman, M., Hosten, N., Howells, F.M., Pol, H.E.H., Huyser, C., Jahanshad, N., James, A., Jernigan, T.L., Jiang, J., Jönsson, E.G., Joska, J.A., Kahn, R., and Klein, M.

Abstract

Contains fulltext : 245396.pdf (Publisher’s version ) (Open Access), Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.

Published
2022

33. Ten years of enhancing neuro-imaging genetics through meta-analysis: An overview from the ENIGMA Genetics Working Group

Author

Medland, S.E., Grasby, K.L., Jahanshad, N., Painter, J.N., Colodro-Conde, L., Bralten, J.B., Hibar, D.P., Lind, P.A., Pizzagalli, F., Thomopoulos, S.I., Stein, J.L., Franke, B., Martin, N.G., Thompson, P.M., Medland, S.E., Grasby, K.L., Jahanshad, N., Painter, J.N., Colodro-Conde, L., Bralten, J.B., Hibar, D.P., Lind, P.A., Pizzagalli, F., Thomopoulos, S.I., Stein, J.L., Franke, B., Martin, N.G., and Thompson, P.M.

Abstract

Contains fulltext : 248938.pdf (Publisher’s version ) (Open Access), Here we review the motivation for creating the enhancing neuroimaging genetics through meta-analysis (ENIGMA) Consortium and the genetic analyses undertaken by the consortium so far. We discuss the methodological challenges, findings, and future directions of the genetics working group. A major goal of the working group is tackling the reproducibility crisis affecting "candidate gene" and genome-wide association analyses in neuroimaging. To address this, we developed harmonized analytic methods, and support their use in coordinated analyses across sites worldwide, which also makes it possible to understand heterogeneity in results across sites. These efforts have resulted in the identification of hundreds of common genomic loci robustly associated with brain structure. We have found both pleiotropic and specific genetic effects associated with brain structures, as well as genetic correlations with psychiatric and neurological diseases.

Published
2022

34. Mapping brain asymmetry in health and disease through the ENIGMA consortium

Author

Kong, X.Z., Postema, M.C., Guadalupe, T., Kovel, C. de, Boedhoe, P.S., Hoogman, M., Mathias, S.R., Rooij, D. van, Schijven, D., Glahn, D.C., Medland, S.E., Jahanshad, N., Thomopoulos, S.I., Turner, J.A., Buitelaar, J.K., Erp, T.G. van, Franke, B., Fisher, S.E., Heuvel, O.A. van den, Schmaal, L., Thompson, P.M., Francks, C., Kong, X.Z., Postema, M.C., Guadalupe, T., Kovel, C. de, Boedhoe, P.S., Hoogman, M., Mathias, S.R., Rooij, D. van, Schijven, D., Glahn, D.C., Medland, S.E., Jahanshad, N., Thomopoulos, S.I., Turner, J.A., Buitelaar, J.K., Erp, T.G. van, Franke, B., Fisher, S.E., Heuvel, O.A. van den, Schmaal, L., Thompson, P.M., and Francks, C.

Abstract

Contains fulltext : 248382.pdf (Publisher’s version ) (Open Access), Left-right asymmetry of the human brain is one of its cardinal features, and also a complex, multivariate trait. Decades of research have suggested that brain asymmetry may be altered in psychiatric disorders. However, findings have been inconsistent and often based on small sample sizes. There are also open questions surrounding which structures are asymmetrical on average in the healthy population, and how variability in brain asymmetry relates to basic biological variables such as age and sex. Over the last 4 years, the ENIGMA-Laterality Working Group has published six studies of gray matter morphological asymmetry based on total sample sizes from roughly 3,500 to 17,000 individuals, which were between one and two orders of magnitude larger than those published in previous decades. A population-level mapping of average asymmetry was achieved, including an intriguing fronto-occipital gradient of cortical thickness asymmetry in healthy brains. ENIGMA's multi-dataset approach also supported an empirical illustration of reproducibility of hemispheric differences across datasets. Effect sizes were estimated for gray matter asymmetry based on large, international, samples in relation to age, sex, handedness, and brain volume, as well as for three psychiatric disorders: autism spectrum disorder was associated with subtly reduced asymmetry of cortical thickness at regions spread widely over the cortex; pediatric obsessive-compulsive disorder was associated with altered subcortical asymmetry; major depressive disorder was not significantly associated with changes of asymmetry. Ongoing studies are examining brain asymmetry in other disorders. Moreover, a groundwork has been laid for possibly identifying shared genetic contributions to brain asymmetry and disorders.

Published
2022

35. Consortium neuroscience of attention deficit/hyperactivity disorder and autism spectrum disorder: The ENIGMA adventure

Author

Hoogman, M., Rooij, D. van, Klein, M., Boedhoe, P., Ilioska, I., Li, T., Patel, Y., Postema, M.C., Zhang-James, Y., Anagnostou, E., Arango, C., Auzias, G., Banaschewski, T., Bau, C.H.D., Behrmann, M., Bellgrove, Mark A., Brandeis, D., Brem, S., Busatto, G.F., Calderoni, S., Calvo, R., Castellanos, F.X., Coghill, D., Conzelmann, A., Daly, E., Deruelle, C., Dinstein, I., Durston, S., Ecker, C., Ehrlich, S., Epstein, J.N., Fair, D.A., Fitzgerald, J., Freitag, C.M., Frodl, T., Gallagher, L., Grevet, E.H., Haavik, J., Hoekstra, P.J., Janssen, J., Karkashadze, G., King, J.A., Konrad, K., Kuntsi, J., Lazaro, L., Lerch, J.P., Lesch, K.P., Louza, M.R., Luna, B., Mattos, P., McGrath, J., Muratori, F., Murphy, C., Nigg, J.T., Oberwelland-Weiss, E., Tuura, R.L. O'Gorman, O'Hearn, K., Oosterlaan, J., Parellada, M., Pauli, P., Plessen, K.J., Ramos-Quiroga, J.A., Reif, A., Reneman, L., Retico, A., Rosa, P.G., Rubia, K., Shaw, P., Silk, T.J., Tamm, L., Vilarroya, O., Walitza, S., Jahanshad, N., Faraone, S.V, Francks, C., Heuvel, O.A. van den, Paus, T., Thompson, P.M., Buitelaar, J.K., Franke, B., Hoogman, M., Rooij, D. van, Klein, M., Boedhoe, P., Ilioska, I., Li, T., Patel, Y., Postema, M.C., Zhang-James, Y., Anagnostou, E., Arango, C., Auzias, G., Banaschewski, T., Bau, C.H.D., Behrmann, M., Bellgrove, Mark A., Brandeis, D., Brem, S., Busatto, G.F., Calderoni, S., Calvo, R., Castellanos, F.X., Coghill, D., Conzelmann, A., Daly, E., Deruelle, C., Dinstein, I., Durston, S., Ecker, C., Ehrlich, S., Epstein, J.N., Fair, D.A., Fitzgerald, J., Freitag, C.M., Frodl, T., Gallagher, L., Grevet, E.H., Haavik, J., Hoekstra, P.J., Janssen, J., Karkashadze, G., King, J.A., Konrad, K., Kuntsi, J., Lazaro, L., Lerch, J.P., Lesch, K.P., Louza, M.R., Luna, B., Mattos, P., McGrath, J., Muratori, F., Murphy, C., Nigg, J.T., Oberwelland-Weiss, E., Tuura, R.L. O'Gorman, O'Hearn, K., Oosterlaan, J., Parellada, M., Pauli, P., Plessen, K.J., Ramos-Quiroga, J.A., Reif, A., Reneman, L., Retico, A., Rosa, P.G., Rubia, K., Shaw, P., Silk, T.J., Tamm, L., Vilarroya, O., Walitza, S., Jahanshad, N., Faraone, S.V, Francks, C., Heuvel, O.A. van den, Paus, T., Thompson, P.M., Buitelaar, J.K., and Franke, B.

Abstract

Contains fulltext : 248364.pdf (Publisher’s version ) (Open Access), Neuroimaging has been extensively used to study brain structure and function in individuals with attention deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) over the past decades. Two of the main shortcomings of the neuroimaging literature of these disorders are the small sample sizes employed and the heterogeneity of methods used. In 2013 and 2014, the ENIGMA-ADHD and ENIGMA-ASD working groups were respectively, founded with a common goal to address these limitations. Here, we provide a narrative review of the thus far completed and still ongoing projects of these working groups. Due to an implicitly hierarchical psychiatric diagnostic classification system, the fields of ADHD and ASD have developed largely in isolation, despite the considerable overlap in the occurrence of the disorders. The collaboration between the ENIGMA-ADHD and -ASD working groups seeks to bring the neuroimaging efforts of the two disorders closer together. The outcomes of case-control studies of subcortical and cortical structures showed that subcortical volumes are similarly affected in ASD and ADHD, albeit with small effect sizes. Cortical analyses identified unique differences in each disorder, but also considerable overlap between the two, specifically in cortical thickness. Ongoing work is examining alternative research questions, such as brain laterality, prediction of case-control status, and anatomical heterogeneity. In brief, great strides have been made toward fulfilling the aims of the ENIGMA collaborations, while new ideas and follow-up analyses continue that include more imaging modalities (diffusion MRI and resting-state functional MRI), collaborations with other large databases, and samples with dual diagnoses.

Published
2022

36. Greater male than female variability in regional brain structure across the lifespan

Author

Wierenga, L.M., Doucet, G.E., Dima, D., Agartz, I., Aghajani, M., Akudjedu, T.N., Albajes-Eizagirre, A., Alnaes, D., Alpert, K.I., Andreassen, O.A., Anticevic, A., Asherson, P., Banaschewski, T., Bargallo, N., Baumeister, S., Baur-Streubel, R., Bertolino, A., Bonvino, A., Boomsma, D.I., Borgwardt, S., Bourque, J., Braber, A., Brandeis, D., Breier, A., Brodaty, H., Brouwer, R.M., Buitelaar, J.K., Busatto, G.F., Calhoun, V.D., Canales-Rodríguez, E.J., Cannon, D.M., Caseras, X., Castellanos, F.X., Chaim-Avancini, T.M., Ching, C.R., Clark, V.P., Conrod, P.J., Conzelmann, A., Crivello, F., Davey, C.G., Dickie, E.W., Ehrlich, S., Ent, D. van 't, Fisher, S.E., Fouche, J.P., Franke, B., Fuentes-Claramonte, P., Geus, E.J. de, Giorgio, A. Di, Glahn, D.C., Gotlib, I.H., Grabe, H.J., Gruber, O., Gruner, P., Gur, R.E., Gur, R.C., Gurholt, T.P., Haan, L. de, Haatveit, B., Harrison, B.J., Hartman, C.A., Hatton, S.N., Heslenfeld, D.J., Heuvel, O.A. van den, Hickie, I.B., Hoekstra, P.J., Hohmann, S., Holmes, A.J., Hoogman, M., Hosten, N., Howells, F.M., Pol, H.E.H., Huyser, C., Jahanshad, N., James, A.C., Jiang, J., Jönsson, E.G., Joska, J.A., Kalnin, A.J., Klein, M., Koenders, L., Kolskår, K.K., Krämer, B., Kuntsi, J., Lagopoulos, J., Lazaro, L., Lebedeva, I.S., Lee, Phil H., Lochner, C., Machielsen, M.W., Maingault, S., Martin, N.G., Martínez-Zalacaín, I., Mataix-Cols, D., Mazoyer, B., McDonald, B.C., McDonald, C., McIntosh, A.M., McMahon, K.L., McPhilemy, G., Naaijen, J., Frangou, S., Tamnes, C.K., Wierenga, L.M., Doucet, G.E., Dima, D., Agartz, I., Aghajani, M., Akudjedu, T.N., Albajes-Eizagirre, A., Alnaes, D., Alpert, K.I., Andreassen, O.A., Anticevic, A., Asherson, P., Banaschewski, T., Bargallo, N., Baumeister, S., Baur-Streubel, R., Bertolino, A., Bonvino, A., Boomsma, D.I., Borgwardt, S., Bourque, J., Braber, A., Brandeis, D., Breier, A., Brodaty, H., Brouwer, R.M., Buitelaar, J.K., Busatto, G.F., Calhoun, V.D., Canales-Rodríguez, E.J., Cannon, D.M., Caseras, X., Castellanos, F.X., Chaim-Avancini, T.M., Ching, C.R., Clark, V.P., Conrod, P.J., Conzelmann, A., Crivello, F., Davey, C.G., Dickie, E.W., Ehrlich, S., Ent, D. van 't, Fisher, S.E., Fouche, J.P., Franke, B., Fuentes-Claramonte, P., Geus, E.J. de, Giorgio, A. Di, Glahn, D.C., Gotlib, I.H., Grabe, H.J., Gruber, O., Gruner, P., Gur, R.E., Gur, R.C., Gurholt, T.P., Haan, L. de, Haatveit, B., Harrison, B.J., Hartman, C.A., Hatton, S.N., Heslenfeld, D.J., Heuvel, O.A. van den, Hickie, I.B., Hoekstra, P.J., Hohmann, S., Holmes, A.J., Hoogman, M., Hosten, N., Howells, F.M., Pol, H.E.H., Huyser, C., Jahanshad, N., James, A.C., Jiang, J., Jönsson, E.G., Joska, J.A., Kalnin, A.J., Klein, M., Koenders, L., Kolskår, K.K., Krämer, B., Kuntsi, J., Lagopoulos, J., Lazaro, L., Lebedeva, I.S., Lee, Phil H., Lochner, C., Machielsen, M.W., Maingault, S., Martin, N.G., Martínez-Zalacaín, I., Mataix-Cols, D., Mazoyer, B., McDonald, B.C., McDonald, C., McIntosh, A.M., McMahon, K.L., McPhilemy, G., Naaijen, J., Frangou, S., and Tamnes, C.K.

Abstract

Contains fulltext : 248376.pdf (Publisher’s version ) (Open Access), For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.

Published
2022

37. In vivo hippocampal subfield volumes in bipolar disorder—A mega-analysis from The Enhancing Neuro Imaging Genetics through Meta-Analysis Bipolar Disorder Working Group

Author

Haukvik, UK, Gurholt, TP, Nerland, S, Elvsåshagen, T, Akudjedu, TN, Alda, M, Alnæs, D, Alonso-Lana, S, Bauer, J, Baune, BT, Benedetti, F, Berk, Michael, Bettella, F, Bøen, E, Bonnín, CM, Brambilla, P, Canales-Rodríguez, EJ, Cannon, DM, Caseras, X, Dandash, O, Dannlowski, U, Delvecchio, G, Díaz-Zuluaga, AM, van Erp, TGM, Fatjó-Vilas, M, Foley, SF, Förster, K, Fullerton, JM, Goikolea, JM, Grotegerd, D, Gruber, O, Haarman, BCM, Haatveit, B, Hajek, T, Hallahan, B, Harris, M, Hawkins, EL, Howells, FM, Hülsmann, C, Jahanshad, N, Jørgensen, KN, Kircher, T, Krämer, B, Krug, A, Kuplicki, R, Lagerberg, TV, Lancaster, TM, Lenroot, RK, Lonning, V, López-Jaramillo, C, Malt, UF, McDonald, C, McIntosh, AM, McPhilemy, G, van der Meer, D, Melle, I, Melloni, EMT, Mitchell, PB, Nabulsi, L, Nenadić, I, Oertel, V, Oldani, L, Opel, N, Otaduy, MCG, Overs, BJ, Pineda-Zapata, JA, Pomarol-Clotet, E, Radua, J, Rauer, L, Redlich, R, Repple, J, Rive, MM, Roberts, G, Ruhe, HG, Salminen, LE, Salvador, R, Sarró, S, Savitz, J, Schene, AH, Sim, K, Soeiro-de-Souza, MG, Stäblein, M, Stein, DJ, Stein, F, Tamnes, CK, Temmingh, HS, Thomopoulos, SI, Veltman, DJ, Vieta, E, Waltemate, L, Westlye, LT, Whalley, HC, Sämann, PG, Thompson, PM, Ching, CRK, Andreassen, OA, Agartz, I, Haukvik, UK, Gurholt, TP, Nerland, S, Elvsåshagen, T, Akudjedu, TN, Alda, M, Alnæs, D, Alonso-Lana, S, Bauer, J, Baune, BT, Benedetti, F, Berk, Michael, Bettella, F, Bøen, E, Bonnín, CM, Brambilla, P, Canales-Rodríguez, EJ, Cannon, DM, Caseras, X, Dandash, O, Dannlowski, U, Delvecchio, G, Díaz-Zuluaga, AM, van Erp, TGM, Fatjó-Vilas, M, Foley, SF, Förster, K, Fullerton, JM, Goikolea, JM, Grotegerd, D, Gruber, O, Haarman, BCM, Haatveit, B, Hajek, T, Hallahan, B, Harris, M, Hawkins, EL, Howells, FM, Hülsmann, C, Jahanshad, N, Jørgensen, KN, Kircher, T, Krämer, B, Krug, A, Kuplicki, R, Lagerberg, TV, Lancaster, TM, Lenroot, RK, Lonning, V, López-Jaramillo, C, Malt, UF, McDonald, C, McIntosh, AM, McPhilemy, G, van der Meer, D, Melle, I, Melloni, EMT, Mitchell, PB, Nabulsi, L, Nenadić, I, Oertel, V, Oldani, L, Opel, N, Otaduy, MCG, Overs, BJ, Pineda-Zapata, JA, Pomarol-Clotet, E, Radua, J, Rauer, L, Redlich, R, Repple, J, Rive, MM, Roberts, G, Ruhe, HG, Salminen, LE, Salvador, R, Sarró, S, Savitz, J, Schene, AH, Sim, K, Soeiro-de-Souza, MG, Stäblein, M, Stein, DJ, Stein, F, Tamnes, CK, Temmingh, HS, Thomopoulos, SI, Veltman, DJ, Vieta, E, Waltemate, L, Westlye, LT, Whalley, HC, Sämann, PG, Thompson, PM, Ching, CRK, Andreassen, OA, and Agartz, I

Published
2022

38. Intracranial and subcortical volumes in adolescents with early-onset psychosis: A multisite mega-analysis from the ENIGMA consortium

Author

Gurholt, TP, Lonning, V, Nerland, S, Jørgensen, KN, Haukvik, UK, Alloza, C, Arango, C, Barth, C, Bearden, CE, Berk, Michael, Bohman, H, Dandash, O, Díaz-Caneja, CM, Edbom, CT, van Erp, TGM, Fett, AKJ, Frangou, S, Goldstein, BI, Grigorian, A, Jahanshad, N, James, AC, Janssen, J, Johannessen, C, Karlsgodt, KH, Kempton, MJ, Kochunov, P, Krabbendam, L, Kyriakopoulos, M, Lundberg, M, MacIntosh, BJ, Rund, BR, Smelror, RE, Sultan, A, Tamnes, CK, Thomopoulos, SI, Vajdi, A, Wedervang-Resell, K, Myhre, AM, Andreassen, OA, Thompson, PM, Agartz, I, Gurholt, TP, Lonning, V, Nerland, S, Jørgensen, KN, Haukvik, UK, Alloza, C, Arango, C, Barth, C, Bearden, CE, Berk, Michael, Bohman, H, Dandash, O, Díaz-Caneja, CM, Edbom, CT, van Erp, TGM, Fett, AKJ, Frangou, S, Goldstein, BI, Grigorian, A, Jahanshad, N, James, AC, Janssen, J, Johannessen, C, Karlsgodt, KH, Kempton, MJ, Kochunov, P, Krabbendam, L, Kyriakopoulos, M, Lundberg, M, MacIntosh, BJ, Rund, BR, Smelror, RE, Sultan, A, Tamnes, CK, Thomopoulos, SI, Vajdi, A, Wedervang-Resell, K, Myhre, AM, Andreassen, OA, Thompson, PM, and Agartz, I

Published
2022

39. Genetic variants associated with longitudinal changes in brain structure across the lifespan

Author

Brouwer, RM, Klein, M, Grasby, KL, Schnack, HG, Jahanshad, N, Teeuw, J, Thomopoulos, SI, Sprooten, E, Franz, CE, Gogtay, N, Kremen, WS, Panizzon, MS, Olde Loohuis, LM, Whelan, CD, Aghajani, M, Alloza, C, Alanaes, D, Artiges, E, Ayesa-Arriola, R, Barker, GJ, Bastin, ME, Blok, E, Boen, E, Breukelaar, IA, Bright, JK, Buimer, EEL, Bulow, R, Cannon, DM, Ciufolini, S, Crossley, NA, Damatac, CG, Dazzan, P, de Mol, CL, de Zwarte, SMC, Desrivieres, S, Diaz-Caneja, CM, Doan, NT, Dohm, K, Froehner, JH, Goltermann, J, Grigis, A, Grotegerd, D, Han, LKM, Harris, MA, Hartman, CA, Heany, SJ, Heindel, W, Heslenfeld, DJ, Hohmann, S, Ittermann, B, Jansen, PR, Janssen, J, Jia, T, Jiang, J, Jockwitz, C, Karali, T, Keeser, D, Koevoets, MGJC, Lenroot, RK, Malchow, B, Mandl, RCW, Medel, V, Meinert, S, Morgan, CA, Muehleisen, TW, Nabulsi, L, Opel, N, de la Foz, VO-G, Overs, BJ, Paillere Martinot, M-L, Redlich, R, Marques, TR, Repple, J, Roberts, G, Roshchupkin, GV, Setiaman, N, Shumskaya, E, Stein, F, Sudre, G, Takahashi, S, Thalamuthu, A, Tordesillas-Gutierrez, D, van der Lugt, A, van Haren, NEM, Wardlaw, JM, Wen, W, Westeneng, H-J, Wittfeld, K, Zhu, AH, Zugman, A, Armstrong, NJ, Bonfiglio, G, Bralten, J, Dalvie, S, Davies, G, Di Forti, M, Ding, L, Donohoe, G, Forstner, AJ, Gonzalez-Penas, J, Guimaraes, JPOFT, Homuth, G, Hottenga, J-J, Knol, MJ, Kwok, JBJ, Le Hellard, S, Mather, KA, Milaneschi, Y, Morris, DW, Noethen, MM, Papiol, S, Rietschel, M, Santoro, ML, Steen, VM, Stein, JL, Streit, F, Tankard, RM, Teumer, A, van 't Ent, D, van der Meer, D, van Eijk, KR, Vassos, E, Vazquez-Bourgon, J, Witt, SH, Adams, HHH, Agartz, I, Ames, D, Amunts, K, Andreassen, OA, Arango, C, Banaschewski, T, Baune, BT, Belangero, SI, Bokde, ALW, Boomsma, DI, Bressan, RA, Brodaty, H, Buitelaar, JK, Cahn, W, Caspers, S, Cichon, S, Crespo-Facorro, B, Cox, SR, Dannlowski, U, Elvsashagen, T, Espeseth, T, Falkai, PG, Fisher, SE, Flor, H, Fullerton, JM, Garavan, H, Gowland, PA, Grabe, HJ, Hahn, T, Heinz, A, Hillegers, M, Hoare, J, Hoekstra, PJ, Ikram, MA, Jackowski, AP, Jansen, A, Jonsson, EG, Kahn, RS, Kircher, T, Korgaonkar, MS, Krug, A, Lemaitre, H, Malt, UF, Martinot, J-L, McDonald, C, Mitchell, PB, Muetzel, RL, Murray, RM, Nees, F, Nenadic, I, Oosterlaan, J, Ophoff, RA, Pan, PM, Penninx, BWJH, Poustka, L, Sachdev, PS, Salum, GA, Schofield, PR, Schumann, G, Shaw, P, Sim, K, Smolka, MN, Stein, DJ, Trollor, JN, van den Berg, LH, Veldink, JH, Walter, H, Westlye, LT, Whelan, R, White, T, Wright, MJ, Medland, SE, Franke, B, Thompson, PM, Hulshoff Pol, HE, Brouwer, RM, Klein, M, Grasby, KL, Schnack, HG, Jahanshad, N, Teeuw, J, Thomopoulos, SI, Sprooten, E, Franz, CE, Gogtay, N, Kremen, WS, Panizzon, MS, Olde Loohuis, LM, Whelan, CD, Aghajani, M, Alloza, C, Alanaes, D, Artiges, E, Ayesa-Arriola, R, Barker, GJ, Bastin, ME, Blok, E, Boen, E, Breukelaar, IA, Bright, JK, Buimer, EEL, Bulow, R, Cannon, DM, Ciufolini, S, Crossley, NA, Damatac, CG, Dazzan, P, de Mol, CL, de Zwarte, SMC, Desrivieres, S, Diaz-Caneja, CM, Doan, NT, Dohm, K, Froehner, JH, Goltermann, J, Grigis, A, Grotegerd, D, Han, LKM, Harris, MA, Hartman, CA, Heany, SJ, Heindel, W, Heslenfeld, DJ, Hohmann, S, Ittermann, B, Jansen, PR, Janssen, J, Jia, T, Jiang, J, Jockwitz, C, Karali, T, Keeser, D, Koevoets, MGJC, Lenroot, RK, Malchow, B, Mandl, RCW, Medel, V, Meinert, S, Morgan, CA, Muehleisen, TW, Nabulsi, L, Opel, N, de la Foz, VO-G, Overs, BJ, Paillere Martinot, M-L, Redlich, R, Marques, TR, Repple, J, Roberts, G, Roshchupkin, GV, Setiaman, N, Shumskaya, E, Stein, F, Sudre, G, Takahashi, S, Thalamuthu, A, Tordesillas-Gutierrez, D, van der Lugt, A, van Haren, NEM, Wardlaw, JM, Wen, W, Westeneng, H-J, Wittfeld, K, Zhu, AH, Zugman, A, Armstrong, NJ, Bonfiglio, G, Bralten, J, Dalvie, S, Davies, G, Di Forti, M, Ding, L, Donohoe, G, Forstner, AJ, Gonzalez-Penas, J, Guimaraes, JPOFT, Homuth, G, Hottenga, J-J, Knol, MJ, Kwok, JBJ, Le Hellard, S, Mather, KA, Milaneschi, Y, Morris, DW, Noethen, MM, Papiol, S, Rietschel, M, Santoro, ML, Steen, VM, Stein, JL, Streit, F, Tankard, RM, Teumer, A, van 't Ent, D, van der Meer, D, van Eijk, KR, Vassos, E, Vazquez-Bourgon, J, Witt, SH, Adams, HHH, Agartz, I, Ames, D, Amunts, K, Andreassen, OA, Arango, C, Banaschewski, T, Baune, BT, Belangero, SI, Bokde, ALW, Boomsma, DI, Bressan, RA, Brodaty, H, Buitelaar, JK, Cahn, W, Caspers, S, Cichon, S, Crespo-Facorro, B, Cox, SR, Dannlowski, U, Elvsashagen, T, Espeseth, T, Falkai, PG, Fisher, SE, Flor, H, Fullerton, JM, Garavan, H, Gowland, PA, Grabe, HJ, Hahn, T, Heinz, A, Hillegers, M, Hoare, J, Hoekstra, PJ, Ikram, MA, Jackowski, AP, Jansen, A, Jonsson, EG, Kahn, RS, Kircher, T, Korgaonkar, MS, Krug, A, Lemaitre, H, Malt, UF, Martinot, J-L, McDonald, C, Mitchell, PB, Muetzel, RL, Murray, RM, Nees, F, Nenadic, I, Oosterlaan, J, Ophoff, RA, Pan, PM, Penninx, BWJH, Poustka, L, Sachdev, PS, Salum, GA, Schofield, PR, Schumann, G, Shaw, P, Sim, K, Smolka, MN, Stein, DJ, Trollor, JN, van den Berg, LH, Veldink, JH, Walter, H, Westlye, LT, Whelan, R, White, T, Wright, MJ, Medland, SE, Franke, B, Thompson, PM, and Hulshoff Pol, HE

Abstract

Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging.

Published
2022

40. A systems-level analysis highlights microglial activation as a modifying factor in common epilepsies

Author

Altmann, A, Ryten, M, Di Nunzio, M, Ravizza, T, Tolomeo, D, Reynolds, RH, Somani, A, Bacigaluppi, M, Iori, V, Micotti, E, Di Sapia, R, Cerovic, M, Palma, E, Ruffolo, G, Botia, JA, Absil, J, Alhusaini, S, Alvim, MKM, Auvinen, P, Bargallo, N, Bartolini, E, Bender, B, Bergo, FPG, Bernardes, T, Bernasconi, A, Bernasconi, N, Bernhardt, BC, Blackmon, K, Braga, B, Caligiuri, ME, Calvo, A, Carlson, C, Carr, SJA, Cavalleri, GL, Cendes, F, Chen, J, Chen, S, Cherubini, A, Concha, L, David, P, Delanty, N, Depondt, C, Devinsky, O, Doherty, CP, Domin, M, Focke, NK, Foley, S, Franca, W, Gambardella, A, Guerrini, R, Hamandi, K, Hibar, DP, Isaev, D, Jackson, GD, Jahanshad, N, Kalviainen, R, Keller, SS, Kochunov, P, Kotikalapudi, R, Kowalczyk, MA, Kuzniecky, R, Kwan, P, Labate, A, Langner, S, Lenge, M, Liu, M, Martin, P, Mascalchi, M, Meletti, S, Morita-Sherman, ME, O'Brien, TJ, Pariente, JC, Richardson, MP, Rodriguez-Cruces, R, Rummel, C, Saavalainen, T, Semmelroch, MK, Severino, M, Striano, P, Thesen, T, Thomas, RH, Tondelli, M, Tortora, D, Vaudano, AE, Vivash, L, Podewils, F, Wagner, J, Weber, B, Wiest, R, Yasuda, CL, Zhang, G, Zhang, J, Leu, C, Avbersek, A, Thom, M, Whelan, CD, Thompson, P, McDonald, CR, Vezzani, A, Sisodiya, SM, Altmann, A, Ryten, M, Di Nunzio, M, Ravizza, T, Tolomeo, D, Reynolds, RH, Somani, A, Bacigaluppi, M, Iori, V, Micotti, E, Di Sapia, R, Cerovic, M, Palma, E, Ruffolo, G, Botia, JA, Absil, J, Alhusaini, S, Alvim, MKM, Auvinen, P, Bargallo, N, Bartolini, E, Bender, B, Bergo, FPG, Bernardes, T, Bernasconi, A, Bernasconi, N, Bernhardt, BC, Blackmon, K, Braga, B, Caligiuri, ME, Calvo, A, Carlson, C, Carr, SJA, Cavalleri, GL, Cendes, F, Chen, J, Chen, S, Cherubini, A, Concha, L, David, P, Delanty, N, Depondt, C, Devinsky, O, Doherty, CP, Domin, M, Focke, NK, Foley, S, Franca, W, Gambardella, A, Guerrini, R, Hamandi, K, Hibar, DP, Isaev, D, Jackson, GD, Jahanshad, N, Kalviainen, R, Keller, SS, Kochunov, P, Kotikalapudi, R, Kowalczyk, MA, Kuzniecky, R, Kwan, P, Labate, A, Langner, S, Lenge, M, Liu, M, Martin, P, Mascalchi, M, Meletti, S, Morita-Sherman, ME, O'Brien, TJ, Pariente, JC, Richardson, MP, Rodriguez-Cruces, R, Rummel, C, Saavalainen, T, Semmelroch, MK, Severino, M, Striano, P, Thesen, T, Thomas, RH, Tondelli, M, Tortora, D, Vaudano, AE, Vivash, L, Podewils, F, Wagner, J, Weber, B, Wiest, R, Yasuda, CL, Zhang, G, Zhang, J, Leu, C, Avbersek, A, Thom, M, Whelan, CD, Thompson, P, McDonald, CR, Vezzani, A, and Sisodiya, SM

Abstract

AIMS: The causes of distinct patterns of reduced cortical thickness in the common human epilepsies, detectable on neuroimaging and with important clinical consequences, are unknown. We investigated the underlying mechanisms of cortical thinning using a systems-level analysis. METHODS: Imaging-based cortical structural maps from a large-scale epilepsy neuroimaging study were overlaid with highly spatially resolved human brain gene expression data from the Allen Human Brain Atlas. Cell-type deconvolution, differential expression analysis and cell-type enrichment analyses were used to identify differences in cell-type distribution. These differences were followed up in post-mortem brain tissue from humans with epilepsy using Iba1 immunolabelling. Furthermore, to investigate a causal effect in cortical thinning, cell-type-specific depletion was used in a murine model of acquired epilepsy. RESULTS: We identified elevated fractions of microglia and endothelial cells in regions of reduced cortical thickness. Differentially expressed genes showed enrichment for microglial markers and, in particular, activated microglial states. Analysis of post-mortem brain tissue from humans with epilepsy confirmed excess activated microglia. In the murine model, transient depletion of activated microglia during the early phase of the disease development prevented cortical thinning and neuronal cell loss in the temporal cortex. Although the development of chronic seizures was unaffected, the epileptic mice with early depletion of activated microglia did not develop deficits in a non-spatial memory test seen in epileptic mice not depleted of microglia. CONCLUSIONS: These convergent data strongly implicate activated microglia in cortical thinning, representing a new dimension for concern and disease modification in the epilepsies, potentially distinct from seizure control.

Published
2022

41. An overview of the first 5 years of the ENIGMA obsessive-compulsive disorder working group: The power of worldwide collaboration

Author

van den Heuvel, OA, Boedhoe, PSW, Bertolin, S, Bruin, WB, Francks, C, Ivanov, I, Jahanshad, N, Kong, X-Z, Kwon, JS, O'Neill, J, Paus, T, Patel, Y, Piras, F, Schmaal, L, Soriano-Mas, C, Spalletta, G, van Wingen, GA, Yun, J-Y, Vriend, C, Simpson, HB, van Rooij, D, Hoexter, MQ, Hoogman, M, Buitelaar, JK, Arnold, P, Beucke, JC, Benedetti, F, Bollettini, I, Bose, A, Brennan, BP, De Nadai, AS, Fitzgerald, K, Gruner, P, Gruenblatt, E, Hirano, Y, Huyser, C, James, A, Koch, K, Kvale, G, Lazaro, L, Lochner, C, Marsh, R, Mataix-Cols, D, Morgado, P, Nakamae, T, Nakao, T, Narayanaswamy, JC, Nurmi, E, Pittenger, C, Reddy, YCJ, Sato, JR, Soreni, N, Stewart, SE, Taylor, SF, Tolin, D, Thomopoulos, SI, Veltman, DJ, Venkatasubramanian, G, Walitza, S, Wang, Z, Thompson, PM, Stein, DJ, van den Heuvel, OA, Boedhoe, PSW, Bertolin, S, Bruin, WB, Francks, C, Ivanov, I, Jahanshad, N, Kong, X-Z, Kwon, JS, O'Neill, J, Paus, T, Patel, Y, Piras, F, Schmaal, L, Soriano-Mas, C, Spalletta, G, van Wingen, GA, Yun, J-Y, Vriend, C, Simpson, HB, van Rooij, D, Hoexter, MQ, Hoogman, M, Buitelaar, JK, Arnold, P, Beucke, JC, Benedetti, F, Bollettini, I, Bose, A, Brennan, BP, De Nadai, AS, Fitzgerald, K, Gruner, P, Gruenblatt, E, Hirano, Y, Huyser, C, James, A, Koch, K, Kvale, G, Lazaro, L, Lochner, C, Marsh, R, Mataix-Cols, D, Morgado, P, Nakamae, T, Nakao, T, Narayanaswamy, JC, Nurmi, E, Pittenger, C, Reddy, YCJ, Sato, JR, Soreni, N, Stewart, SE, Taylor, SF, Tolin, D, Thomopoulos, SI, Veltman, DJ, Venkatasubramanian, G, Walitza, S, Wang, Z, Thompson, PM, and Stein, DJ

Abstract

Neuroimaging has played an important part in advancing our understanding of the neurobiology of obsessive-compulsive disorder (OCD). At the same time, neuroimaging studies of OCD have had notable limitations, including reliance on relatively small samples. International collaborative efforts to increase statistical power by combining samples from across sites have been bolstered by the ENIGMA consortium; this provides specific technical expertise for conducting multi-site analyses, as well as access to a collaborative community of neuroimaging scientists. In this article, we outline the background to, development of, and initial findings from ENIGMA's OCD working group, which currently consists of 47 samples from 34 institutes in 15 countries on 5 continents, with a total sample of 2,323 OCD patients and 2,325 healthy controls. Initial work has focused on studies of cortical thickness and subcortical volumes, structural connectivity, and brain lateralization in children, adolescents and adults with OCD, also including the study on the commonalities and distinctions across different neurodevelopment disorders. Additional work is ongoing, employing machine learning techniques. Findings to date have contributed to the development of neurobiological models of OCD, have provided an important model of global scientific collaboration, and have had a number of clinical implications. Importantly, our work has shed new light on questions about whether structural and functional alterations found in OCD reflect neurodevelopmental changes, effects of the disease process, or medication impacts. We conclude with a summary of ongoing work by ENIGMA-OCD, and a consideration of future directions for neuroimaging research on OCD within and beyond ENIGMA.

Published
2022

42. How do substance use disorders compare to other psychiatric conditions on structural brain abnormalities? A cross-disorder meta-analytic comparison using theENIGMAconsortium findings

Author

Navarri, X, Afzali, MH, Lavoie, J, Sinha, R, Stein, DJ, Momenan, R, Veltman, DJ, Korucuoglu, O, Sjoerds, Z, van Holst, RJ, Hester, R, Orr, C, Cousijn, J, Yucel, M, Lorenzetti, V, Wiers, R, Jahanshad, N, Glahn, DC, Thompson, PM, Mackey, S, Conrod, PJ, Navarri, X, Afzali, MH, Lavoie, J, Sinha, R, Stein, DJ, Momenan, R, Veltman, DJ, Korucuoglu, O, Sjoerds, Z, van Holst, RJ, Hester, R, Orr, C, Cousijn, J, Yucel, M, Lorenzetti, V, Wiers, R, Jahanshad, N, Glahn, DC, Thompson, PM, Mackey, S, and Conrod, PJ

Abstract

Alcohol use disorder (AUD) and cannabis use disorder (CUD) are associated with brain alterations particularly involving fronto-cerebellar and meso-cortico-limbic circuitry. However, such abnormalities have additionally been reported in other psychiatric conditions, and until recently there has been few large-scale investigations to compare such findings. The current study uses the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium method of standardising structural brain measures to quantify case-control differences and to compare brain-correlates of substance use disorders with those published in relation to other psychiatric disorders. Using the ENIGMA protocols, we report effect sizes derived from a meta-analysis of alcohol (seven studies, N = 798, 54% are cases) and cannabis (seven studies, N = 447, 45% are cases) dependent cases and age- and sex-matched controls. We conduct linear analyses using harmonised methods to process and parcellate brain data identical to those reported in the literature for ENIGMA case-control studies of major depression disorder (MDD), schizophrenia (SCZ) and bipolar disorder so that effect sizes are optimally comparable across disorders. R elationships between substance use disorder diagnosis and subcortical grey matter volumes and cortical thickness were assessed with intracranial volume, age and sex as co-variates . After correcting for multiple comparisons, AUD case-control meta-analysis of subcortical regions indicated significant differences in the thalamus, hippocampus, amygdala and accumbens, with effect sizes (0.23) generally equivalent to, or larger than |0.23| those previously reported for other psychiatric disorders (except for the pallidum and putamen). On measures of cortical thickness, AUD was associated with significant differences bilaterally in the fusiform gyrus, inferior temporal gyrus, temporal pole, superior frontal gyrus, and rostral and caudal anterior cingulate gyri. Meta-analysis of CUD

Published
2022

43. The Enhancing NeuroImaging Genetics through Meta-Analysis Consortium: 10 Years of Global Collaborations in Human Brain Mapping

Author

Thompson, PM, Jahanshad, N, Schmaal, L, Turner, JA, Winkler, AM, Thomopoulos, SI, Egan, GF, Kochunov, P, Thompson, PM, Jahanshad, N, Schmaal, L, Turner, JA, Winkler, AM, Thomopoulos, SI, Egan, GF, and Kochunov, P

Abstract

This Special Issue of Human Brain Mapping is dedicated to a 10-year anniversary of the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium. It reports updates from a broad range of international neuroimaging projects that pool data from around the world to answer fundamental questions in neuroscience. Since ENIGMA was formed in December 2009, the initiative grew into a worldwide effort with over 2,000 participating scientists from 45 countries, and over 50 working groups leading large-scale studies of human brain disorders. Over the last decade, many lessons were learned on how best to pool brain data from diverse sources. Working groups were created to develop methods to analyze worldwide data from anatomical and diffusion magnetic resonance imaging (MRI), resting state and task-based functional MRI, electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance spectroscopy (MRS). The quest to understand genetic effects on human brain development and disease also led to analyses of brain scans on an unprecedented scale. Genetic roadmaps of the human cortex were created by researchers worldwide who collaborated to perform statistically well-powered analyses of common and rare genetic variants on brain measures and rates of brain development and aging. Here, we summarize the 31 papers in this Special Issue, covering: (a) technical approaches to harmonize analysis of different types of brain imaging data, (b) reviews of the last decade of work by several of ENIGMA's clinical and technical working groups, and (c) new empirical papers reporting large-scale international brain mapping analyses in patients with substance use disorders, schizophrenia, bipolar disorders, major depression, posttraumatic stress disorder, obsessive compulsive disorder, epilepsy, and stroke.

Published
2022

44. Greater male than female variability in regional brain structure across the lifespan

Author

Wierenga, LM, Doucet, GE, Dima, D, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andreassen, OA, Anticevic, A, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, den Braber, A, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Calhoun, VD, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Chaim-Avancini, TM, Ching, CRK, Clark, VP, Conrod, PJ, Conzelmann, A, Crivello, F, Davey, CG, Dickie, EW, Ehrlich, S, Van't Ent, D, Fisher, SE, Fouche, J-P, Franke, B, Fuentes-Claramonte, P, de Geus, EJC, Di Giorgio, A, Glahn, DC, Gotlib, IH, Grabe, HJ, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Gurholt, TP, de Haan, L, Haatveit, B, Harrison, BJ, Hartman, CA, Hatton, SN, Heslenfeld, DJ, van den Heuvel, OA, Hickie, IB, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, AC, Jiang, J, Jonsson, EG, Joska, JA, Kalnin, AJ, Klein, M, Koenders, L, Kolskar, KK, Kramer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, IS, Lee, PH, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, BC, McDonald, C, McIntosh, AM, McMahon, KL, McPhilemy, G, van der Meer, D, Menchon, JM, Naaijen, J, Nyberg, L, Oosterlaan, J, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Radua, J, Reif, A, Richard, G, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Sim, K, Simmons, A, Smoller, JW, Sommer, IE, Soriano-Mas, C, Stein, DJ, Strike, LT, Szeszko, PR, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Trollor, JN, Uhlmann, A, Veer, IM, Veltman, DJ, Voineskos, A, Volzke, H, Walter, H, Wang, L, Wang, Y, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, HC, Williams, SCR, Wittfeld, K, Wolf, DH, Wright, MJ, Yoncheva, YN, Zanetti, M, Ziegler, GC, de Zubicaray, G, Thompson, PM, Crone, EA, Frangou, S, Tamnes, CK, Wierenga, LM, Doucet, GE, Dima, D, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andreassen, OA, Anticevic, A, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, den Braber, A, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Calhoun, VD, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Chaim-Avancini, TM, Ching, CRK, Clark, VP, Conrod, PJ, Conzelmann, A, Crivello, F, Davey, CG, Dickie, EW, Ehrlich, S, Van't Ent, D, Fisher, SE, Fouche, J-P, Franke, B, Fuentes-Claramonte, P, de Geus, EJC, Di Giorgio, A, Glahn, DC, Gotlib, IH, Grabe, HJ, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Gurholt, TP, de Haan, L, Haatveit, B, Harrison, BJ, Hartman, CA, Hatton, SN, Heslenfeld, DJ, van den Heuvel, OA, Hickie, IB, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, AC, Jiang, J, Jonsson, EG, Joska, JA, Kalnin, AJ, Klein, M, Koenders, L, Kolskar, KK, Kramer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, IS, Lee, PH, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, BC, McDonald, C, McIntosh, AM, McMahon, KL, McPhilemy, G, van der Meer, D, Menchon, JM, Naaijen, J, Nyberg, L, Oosterlaan, J, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Radua, J, Reif, A, Richard, G, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Sim, K, Simmons, A, Smoller, JW, Sommer, IE, Soriano-Mas, C, Stein, DJ, Strike, LT, Szeszko, PR, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Trollor, JN, Uhlmann, A, Veer, IM, Veltman, DJ, Voineskos, A, Volzke, H, Walter, H, Wang, L, Wang, Y, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, HC, Williams, SCR, Wittfeld, K, Wolf, DH, Wright, MJ, Yoncheva, YN, Zanetti, M, Ziegler, GC, de Zubicaray, G, Thompson, PM, Crone, EA, Frangou, S, and Tamnes, CK

Abstract

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.

Published
2022

45. The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke

Author

Liew, S-L, Zavaliangos-Petropulu, A, Jahanshad, N, Lang, CE, Hayward, KS, Lohse, KR, Juliano, JM, Assogna, F, Baugh, LA, Bhattacharya, AK, Bigjahan, B, Borich, MR, Boyd, LA, Brodtmann, A, Buetefisch, CM, Byblow, WD, Cassidy, JM, Conforto, AB, Craddock, RC, Dimyan, MA, Dula, AN, Ermer, E, Etherton, MR, Fercho, KA, Gregory, CM, Hadidchi, S, Holguin, JA, Hwang, DH, Jung, S, Kautz, SA, Khlif, MS, Khoshab, N, Kim, B, Kim, H, Kuceyeski, A, Lotze, M, MacIntosh, BJ, Margetis, JL, Mohamed, FB, Piras, F, Ramos-Murguialday, A, Richard, G, Roberts, P, Robertson, AD, Rondina, JM, Rost, NS, Sanossian, N, Schweighofer, N, Seo, NJ, Shiroishi, MS, Soekadar, SR, Spalletta, G, Stinear, CM, Suri, A, Tang, WKW, Thielman, GT, Vecchio, D, Villringer, A, Ward, NS, Werden, E, Westlye, LT, Winstein, C, Wittenberg, GF, Wong, KA, Yu, C, Cramer, SC, Thompson, PM, Liew, S-L, Zavaliangos-Petropulu, A, Jahanshad, N, Lang, CE, Hayward, KS, Lohse, KR, Juliano, JM, Assogna, F, Baugh, LA, Bhattacharya, AK, Bigjahan, B, Borich, MR, Boyd, LA, Brodtmann, A, Buetefisch, CM, Byblow, WD, Cassidy, JM, Conforto, AB, Craddock, RC, Dimyan, MA, Dula, AN, Ermer, E, Etherton, MR, Fercho, KA, Gregory, CM, Hadidchi, S, Holguin, JA, Hwang, DH, Jung, S, Kautz, SA, Khlif, MS, Khoshab, N, Kim, B, Kim, H, Kuceyeski, A, Lotze, M, MacIntosh, BJ, Margetis, JL, Mohamed, FB, Piras, F, Ramos-Murguialday, A, Richard, G, Roberts, P, Robertson, AD, Rondina, JM, Rost, NS, Sanossian, N, Schweighofer, N, Seo, NJ, Shiroishi, MS, Soekadar, SR, Spalletta, G, Stinear, CM, Suri, A, Tang, WKW, Thielman, GT, Vecchio, D, Villringer, A, Ward, NS, Werden, E, Westlye, LT, Winstein, C, Wittenberg, GF, Wong, KA, Yu, C, Cramer, SC, and Thompson, PM

Abstract

The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.

Published
2022

46. In vivo hippocampal subfield volumes in bipolar disorder-A mega-analysis from The Enhancing Neuro Imaging Genetics throughMeta-AnalysisBipolar Disorder Working Group

Author

Haukvik, UK, Gurholt, TP, Nerland, S, Elvsashagen, T, Akudjedu, TN, Alda, M, Alnaes, D, Alonso-Lana, S, Bauer, J, Baune, BT, Benedetti, F, Berk, M, Bettella, F, Boen, E, Bonnin, CM, Brambilla, P, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Dandash, O, Dannlowski, U, Delvecchio, G, Diaz-Zuluaga, AM, Erp, TGM, Fatjo-Vilas, M, Foley, SF, Foerster, K, Fullerton, JM, Goikolea, JM, Grotegerd, D, Gruber, O, Haarman, BCM, Haatveit, B, Hajek, T, Hallahan, B, Harris, M, Hawkins, EL, Howells, FM, Huelsmann, C, Jahanshad, N, Jorgensen, KN, Kircher, T, Kraemer, B, Krug, A, Kuplicki, R, Lagerberg, T, Lancaster, TM, Lenroot, RK, Lonning, V, Lopez-Jaramillo, C, Malt, UF, McDonald, C, McIntosh, AM, McPhilemy, G, Meer, D, Melle, I, Melloni, EMT, Mitchell, PB, Nabulsi, L, Nenadic, I, Oertel, V, Oldani, L, Opel, N, Otaduy, MCG, Overs, BJ, Pineda-Zapata, JA, Pomarol-Clotet, E, Radua, J, Rauer, L, Redlich, R, Repple, J, Rive, MM, Roberts, G, Ruhe, HG, Salminen, LE, Salvador, R, Sarro, S, Savitz, J, Schene, AH, Sim, K, Soeiro-de-Souza, MG, Staeblein, M, Stein, DJ, Stein, F, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Veltman, DJ, Vieta, E, Waltemate, L, Westlye, LT, Whalley, HC, Saemann, PG, Thompson, PM, Ching, CRK, Andreassen, OA, Agartz, I, Haukvik, UK, Gurholt, TP, Nerland, S, Elvsashagen, T, Akudjedu, TN, Alda, M, Alnaes, D, Alonso-Lana, S, Bauer, J, Baune, BT, Benedetti, F, Berk, M, Bettella, F, Boen, E, Bonnin, CM, Brambilla, P, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Dandash, O, Dannlowski, U, Delvecchio, G, Diaz-Zuluaga, AM, Erp, TGM, Fatjo-Vilas, M, Foley, SF, Foerster, K, Fullerton, JM, Goikolea, JM, Grotegerd, D, Gruber, O, Haarman, BCM, Haatveit, B, Hajek, T, Hallahan, B, Harris, M, Hawkins, EL, Howells, FM, Huelsmann, C, Jahanshad, N, Jorgensen, KN, Kircher, T, Kraemer, B, Krug, A, Kuplicki, R, Lagerberg, T, Lancaster, TM, Lenroot, RK, Lonning, V, Lopez-Jaramillo, C, Malt, UF, McDonald, C, McIntosh, AM, McPhilemy, G, Meer, D, Melle, I, Melloni, EMT, Mitchell, PB, Nabulsi, L, Nenadic, I, Oertel, V, Oldani, L, Opel, N, Otaduy, MCG, Overs, BJ, Pineda-Zapata, JA, Pomarol-Clotet, E, Radua, J, Rauer, L, Redlich, R, Repple, J, Rive, MM, Roberts, G, Ruhe, HG, Salminen, LE, Salvador, R, Sarro, S, Savitz, J, Schene, AH, Sim, K, Soeiro-de-Souza, MG, Staeblein, M, Stein, DJ, Stein, F, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Veltman, DJ, Vieta, E, Waltemate, L, Westlye, LT, Whalley, HC, Saemann, PG, Thompson, PM, Ching, CRK, Andreassen, OA, and Agartz, I

Abstract

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD.

Published
2022

47. FreeSurfer-based segmentation of hippocampal subfields: A review of methods and applications, with a novel quality control procedure for ENIGMA studies and other collaborative efforts

Author

Samann, PG, Iglesias, JE, Gutman, B, Grotegerd, D, Leenings, R, Flint, C, Dannlowski, U, Clarke-Rubright, EK, Morey, RA, van Erp, TGM, Whelan, CD, Han, LKM, van Velzen, LS, Cao, B, Augustinack, JC, Thompson, PM, Jahanshad, N, Schmaal, L, Samann, PG, Iglesias, JE, Gutman, B, Grotegerd, D, Leenings, R, Flint, C, Dannlowski, U, Clarke-Rubright, EK, Morey, RA, van Erp, TGM, Whelan, CD, Han, LKM, van Velzen, LS, Cao, B, Augustinack, JC, Thompson, PM, Jahanshad, N, and Schmaal, L

Abstract

Structural hippocampal abnormalities are common in many neurological and psychiatric disorders, and variation in hippocampal measures is related to cognitive performance and other complex phenotypes such as stress sensitivity. Hippocampal subregions are increasingly studied, as automated algorithms have become available for mapping and volume quantification. In the context of the Enhancing Neuro Imaging Genetics through Meta Analysis Consortium, several Disease Working Groups are using the FreeSurfer software to analyze hippocampal subregion (subfield) volumes in patients with neurological and psychiatric conditions along with data from matched controls. In this overview, we explain the algorithm's principles, summarize measurement reliability studies, and demonstrate two additional aspects (subfield autocorrelation and volume/reliability correlation) with illustrative data. We then explain the rationale for a standardized hippocampal subfield segmentation quality control (QC) procedure for improved pipeline harmonization. To guide researchers to make optimal use of the algorithm, we discuss how global size and age effects can be modeled, how QC steps can be incorporated and how subfields may be aggregated into composite volumes. This discussion is based on a synopsis of 162 published neuroimaging studies (01/2013-12/2019) that applied the FreeSurfer hippocampal subfield segmentation in a broad range of domains including cognition and healthy aging, brain development and neurodegeneration, affective disorders, psychosis, stress regulation, neurotoxicity, epilepsy, inflammatory disease, childhood adversity and posttraumatic stress disorder, and candidate and whole genome (epi-)genetics. Finally, we highlight points where FreeSurfer-based hippocampal subfield studies may be optimized.

Published
2022

48. Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years

Author

Dima, D, Modabbernia, A, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, Ortiz-Garcia De la Foz, V, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, Van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Williams, SCR, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, Frangou, S, Dima, D, Modabbernia, A, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, Ortiz-Garcia De la Foz, V, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, Van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Williams, SCR, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, and Frangou, S

Abstract

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.

Published
2022

49. Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years

Author

Frangou, S, Modabbernia, A, Williams, SCR, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, de la Foz, VO-G, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, Dima, D, Frangou, S, Modabbernia, A, Williams, SCR, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, de la Foz, VO-G, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, and Dima, D

Abstract

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.

Published
2022

50. ENIGMA-anxiety working group: Rationale for and organization of large-scale neuroimaging studies of anxiety disorders.

Author

Bas-Hoogendam, JM, Groenewold, NA, Aghajani, M, Freitag, GF, Harrewijn, A, Hilbert, K, Jahanshad, N, Thomopoulos, SI, Thompson, PM, Veltman, DJ, Winkler, AM, Lueken, U, Pine, DS, van der Wee, NJA, Stein, DJ, ENIGMA-Anxiety Working Group, Bas-Hoogendam, JM, Groenewold, NA, Aghajani, M, Freitag, GF, Harrewijn, A, Hilbert, K, Jahanshad, N, Thomopoulos, SI, Thompson, PM, Veltman, DJ, Winkler, AM, Lueken, U, Pine, DS, van der Wee, NJA, Stein, DJ, and ENIGMA-Anxiety Working Group

Abstract

Anxiety disorders are highly prevalent and disabling but seem particularly tractable to investigation with translational neuroscience methodologies. Neuroimaging has informed our understanding of the neurobiology of anxiety disorders, but research has been limited by small sample sizes and low statistical power, as well as heterogenous imaging methodology. The ENIGMA-Anxiety Working Group has brought together researchers from around the world, in a harmonized and coordinated effort to address these challenges and generate more robust and reproducible findings. This paper elaborates on the concepts and methods informing the work of the working group to date, and describes the initial approach of the four subgroups studying generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobia. At present, the ENIGMA-Anxiety database contains information about more than 100 unique samples, from 16 countries and 59 institutes. Future directions include examining additional imaging modalities, integrating imaging and genetic data, and collaborating with other ENIGMA working groups. The ENIGMA consortium creates synergy at the intersection of global mental health and clinical neuroscience, and the ENIGMA-Anxiety Working Group extends the promise of this approach to neuroimaging research on anxiety disorders.

Published
2022

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